Dataset statistics
| Number of variables | 42 |
|---|---|
| Number of observations | 742 |
| Missing cells | 0 |
| Missing cells (%) | 0.0% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 243.6 KiB |
| Average record size in memory | 336.2 B |
Variable types
| Numeric | 7 |
|---|---|
| Categorical | 35 |
Job Title has a high cardinality: 264 distinct values | High cardinality |
Salary Estimate has a high cardinality: 416 distinct values | High cardinality |
Job Description has a high cardinality: 463 distinct values | High cardinality |
Company Name has a high cardinality: 343 distinct values | High cardinality |
Location has a high cardinality: 200 distinct values | High cardinality |
Headquarters has a high cardinality: 198 distinct values | High cardinality |
Industry has a high cardinality: 60 distinct values | High cardinality |
Competitors has a high cardinality: 128 distinct values | High cardinality |
company_txt has a high cardinality: 343 distinct values | High cardinality |
Founded is highly correlated with Size and 4 other fields | High correlation |
Lower Salary is highly correlated with Industry and 3 other fields | High correlation |
Upper Salary is highly correlated with Industry and 5 other fields | High correlation |
Avg Salary(K) is highly correlated with Industry and 4 other fields | High correlation |
Age is highly correlated with Size and 5 other fields | High correlation |
spark is highly correlated with Industry and 3 other fields | High correlation |
keras is highly correlated with pytorch and 2 other fields | High correlation |
pytorch is highly correlated with keras and 2 other fields | High correlation |
scikit is highly correlated with keras and 2 other fields | High correlation |
tensor is highly correlated with keras and 2 other fields | High correlation |
hadoop is highly correlated with spark and 1 other fields | High correlation |
Employer provided is highly correlated with Rating and 3 other fields | High correlation |
Type of ownership is highly correlated with Industry and 3 other fields | High correlation |
job_title_sim is highly correlated with Industry and 10 other fields | High correlation |
Python is highly correlated with Industry and 3 other fields | High correlation |
Size is highly correlated with Rating and 7 other fields | High correlation |
Hourly is highly correlated with Industry and 3 other fields | High correlation |
Industry is highly correlated with Rating and 18 other fields | High correlation |
Sector is highly correlated with Rating and 13 other fields | High correlation |
google_an is highly correlated with Industry and 1 other fields | High correlation |
Job Location is highly correlated with Size and 9 other fields | High correlation |
sql is highly correlated with Industry and 4 other fields | High correlation |
Rating is highly correlated with Size and 3 other fields | High correlation |
Revenue is highly correlated with Size and 6 other fields | High correlation |
tableau is highly correlated with sql and 2 other fields | High correlation |
bi is highly correlated with tableau | High correlation |
Degree is highly correlated with Industry and 1 other fields | High correlation |
Salary Estimate is uniformly distributed | Uniform |
Job Description is uniformly distributed | Uniform |
df_index has unique values | Unique |
Reproduction
| Analysis started | 2022-10-02 04:33:28.768715 |
|---|---|
| Analysis finished | 2022-10-02 04:34:06.505287 |
| Duration | 37.74 seconds |
| Software version | pandas-profiling v3.3.0 |
| Download configuration | config.json |
| Distinct | 742 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 469.1293801 |
| Minimum | 0 |
|---|---|
| Maximum | 955 |
| Zeros | 1 |
| Zeros (%) | 0.1% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 5.9 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 38.05 |
| Q1 | 221.5 |
| median | 472.5 |
| Q3 | 707.75 |
| 95-th percentile | 908.9 |
| Maximum | 955 |
| Range | 955 |
| Interquartile range (IQR) | 486.25 |
Descriptive statistics
| Standard deviation | 279.7931171 |
|---|---|
| Coefficient of variation (CV) | 0.5964092828 |
| Kurtosis | -1.215840104 |
| Mean | 469.1293801 |
| Median Absolute Deviation (MAD) | 244 |
| Skewness | 0.004952491596 |
| Sum | 348094 |
| Variance | 78284.18837 |
| Monotonicity | Strictly increasing |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 0 | 1 | 0.1% |
| 638 | 1 | 0.1% |
| 629 | 1 | 0.1% |
| 630 | 1 | 0.1% |
| 631 | 1 | 0.1% |
| 632 | 1 | 0.1% |
| 633 | 1 | 0.1% |
| 634 | 1 | 0.1% |
| 635 | 1 | 0.1% |
| 636 | 1 | 0.1% |
| Other values (732) | 732 |
| Value | Count | Frequency (%) |
| 0 | 1 | |
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 1 |
| Value | Count | Frequency (%) |
| 955 | 1 | |
| 953 | 1 | |
| 952 | 1 | |
| 951 | 1 | |
| 950 | 1 | |
| 949 | 1 | |
| 948 | 1 | |
| 947 | 1 | |
| 946 | 1 | |
| 945 | 1 |
| Distinct | 264 |
|---|---|
| Distinct (%) | 35.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| Data Scientist | |
|---|---|
| Data Engineer | |
| Senior Data Scientist | 34 |
| Data Analyst | 15 |
| Senior Data Engineer | 14 |
| Other values (259) |
Length
| Max length | 98 |
|---|---|
| Median length | 66 |
| Mean length | 27.94204852 |
| Min length | 9 |
Characters and Unicode
| Total characters | 20733 |
|---|---|
| Distinct characters | 66 |
| Distinct categories | 9 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 2 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 106 ? |
|---|---|
| Unique (%) | 14.3% |
Sample
| 1st row | Data Scientist |
|---|---|
| 2nd row | Healthcare Data Scientist |
| 3rd row | Data Scientist |
| 4th row | Data Scientist |
| 5th row | Data Scientist |
Common Values
| Value | Count | Frequency (%) |
| Data Scientist | 131 | 17.7% |
| Data Engineer | 53 | 7.1% |
| Senior Data Scientist | 34 | 4.6% |
| Data Analyst | 15 | 2.0% |
| Senior Data Engineer | 14 | 1.9% |
| Senior Data Analyst | 12 | 1.6% |
| Lead Data Scientist | 8 | 1.1% |
| Marketing Data Analyst | 6 | 0.8% |
| Sr. Data Engineer | 6 | 0.8% |
| Machine Learning Engineer | 5 | 0.7% |
| Other values (254) | 458 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| data | 567 | |
| scientist | 420 | 14.9% |
| 173 | 6.1% | |
| engineer | 160 | 5.7% |
| senior | 124 | 4.4% |
| analyst | 102 | 3.6% |
| sr | 48 | 1.7% |
| analytics | 38 | 1.3% |
| science | 36 | 1.3% |
| associate | 33 | 1.2% |
| Other values (313) | 1122 |
Most occurring characters
| Value | Count | Frequency (%) |
| t | 2114 | 10.2% |
| 2081 | 10.0% | |
| a | 2004 | 9.7% |
| i | 1852 | 8.9% |
| e | 1744 | 8.4% |
| n | 1605 | 7.7% |
| c | 940 | 4.5% |
| s | 938 | 4.5% |
| r | 857 | 4.1% |
| S | 799 | 3.9% |
| Other values (56) | 5799 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 14912 | |
| Uppercase Letter | 3194 | 15.4% |
| Space Separator | 2081 | 10.0% |
| Other Punctuation | 228 | 1.1% |
| Dash Punctuation | 193 | 0.9% |
| Open Punctuation | 42 | 0.2% |
| Close Punctuation | 41 | 0.2% |
| Decimal Number | 41 | 0.2% |
| Math Symbol | 1 | < 0.1% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| t | 2114 | |
| a | 2004 | |
| i | 1852 | |
| e | 1744 | |
| n | 1605 | |
| c | 940 | |
| s | 938 | |
| r | 857 | |
| o | 641 | 4.3% |
| l | 532 | 3.6% |
| Other values (15) | 1685 |
Uppercase Letter
| Value | Count | Frequency (%) |
| S | 799 | |
| D | 661 | |
| A | 290 | 9.1% |
| E | 261 | 8.2% |
| M | 151 | 4.7% |
| I | 150 | 4.7% |
| C | 136 | 4.3% |
| L | 118 | 3.7% |
| P | 111 | 3.5% |
| R | 102 | 3.2% |
| Other values (14) | 415 |
Decimal Number
| Value | Count | Frequency (%) |
| 2 | 14 | |
| 0 | 12 | |
| 1 | 8 | |
| 4 | 3 | 7.3% |
| 5 | 2 | 4.9% |
| 9 | 2 | 4.9% |
Other Punctuation
| Value | Count | Frequency (%) |
| , | 103 | |
| / | 62 | |
| . | 35 | 15.4% |
| & | 26 | 11.4% |
| : | 2 | 0.9% |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 184 | |
| – | 9 | 4.7% |
Space Separator
| Value | Count | Frequency (%) |
| 2081 |
Open Punctuation
| Value | Count | Frequency (%) |
| ( | 42 |
Close Punctuation
| Value | Count | Frequency (%) |
| ) | 41 |
Math Symbol
| Value | Count | Frequency (%) |
| | | 1 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 18106 | |
| Common | 2627 | 12.7% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| t | 2114 | |
| a | 2004 | |
| i | 1852 | |
| e | 1744 | 9.6% |
| n | 1605 | 8.9% |
| c | 940 | 5.2% |
| s | 938 | 5.2% |
| r | 857 | 4.7% |
| S | 799 | 4.4% |
| D | 661 | 3.7% |
| Other values (39) | 4592 |
Common
| Value | Count | Frequency (%) |
| 2081 | ||
| - | 184 | 7.0% |
| , | 103 | 3.9% |
| / | 62 | 2.4% |
| ( | 42 | 1.6% |
| ) | 41 | 1.6% |
| . | 35 | 1.3% |
| & | 26 | 1.0% |
| 2 | 14 | 0.5% |
| 0 | 12 | 0.5% |
| Other values (7) | 27 | 1.0% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 20724 | |
| Punctuation | 9 | < 0.1% |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| t | 2114 | 10.2% |
| 2081 | 10.0% | |
| a | 2004 | 9.7% |
| i | 1852 | 8.9% |
| e | 1744 | 8.4% |
| n | 1605 | 7.7% |
| c | 940 | 4.5% |
| s | 938 | 4.5% |
| r | 857 | 4.1% |
| S | 799 | 3.9% |
| Other values (55) | 5790 |
Punctuation
| Value | Count | Frequency (%) |
| – | 9 |
| Distinct | 416 |
|---|---|
| Distinct (%) | 56.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| $49K-$113K (Glassdoor est.) | 6 |
|---|---|
| $86K-$143K (Glassdoor est.) | 6 |
| $54K-$115K (Glassdoor est.) | 6 |
| $21-$34 Per Hour(Glassdoor est.) | 6 |
| $74K-$124K (Glassdoor est.) | 5 |
| Other values (411) |
Length
| Max length | 41 |
|---|---|
| Median length | 27 |
| Mean length | 27.26684636 |
| Min length | 24 |
Characters and Unicode
| Total characters | 20232 |
|---|---|
| Distinct characters | 37 |
| Distinct categories | 9 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 196 ? |
|---|---|
| Unique (%) | 26.4% |
Sample
| 1st row | $53K-$91K (Glassdoor est.) |
|---|---|
| 2nd row | $63K-$112K (Glassdoor est.) |
| 3rd row | $80K-$90K (Glassdoor est.) |
| 4th row | $56K-$97K (Glassdoor est.) |
| 5th row | $86K-$143K (Glassdoor est.) |
Common Values
| Value | Count | Frequency (%) |
| $49K-$113K (Glassdoor est.) | 6 | 0.8% |
| $86K-$143K (Glassdoor est.) | 6 | 0.8% |
| $54K-$115K (Glassdoor est.) | 6 | 0.8% |
| $21-$34 Per Hour(Glassdoor est.) | 6 | 0.8% |
| $74K-$124K (Glassdoor est.) | 5 | 0.7% |
| $76K-$142K (Glassdoor est.) | 5 | 0.7% |
| $107K-$173K (Glassdoor est.) | 5 | 0.7% |
| $81K-$167K (Glassdoor est.) | 5 | 0.7% |
| $68K-$139K (Glassdoor est.) | 4 | 0.5% |
| $63K-$105K (Glassdoor est.) | 4 | 0.5% |
| Other values (406) | 690 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| est | 725 | |
| glassdoor | 692 | |
| per | 24 | 1.1% |
| hour(glassdoor | 21 | 0.9% |
| provided | 17 | 0.8% |
| employer | 17 | 0.8% |
| 49k-$113k | 6 | 0.3% |
| 86k-$143k | 6 | 0.3% |
| 54k-$115k | 6 | 0.3% |
| 21-$34 | 6 | 0.3% |
| Other values (413) | 721 |
Most occurring characters
| Value | Count | Frequency (%) |
| s | 2151 | 10.6% |
| 1499 | 7.4% | |
| o | 1496 | 7.4% |
| $ | 1484 | 7.3% |
| K | 1436 | 7.1% |
| 1 | 919 | 4.5% |
| r | 824 | 4.1% |
| e | 795 | 3.9% |
| l | 759 | 3.8% |
| a | 747 | 3.7% |
| Other values (27) | 8122 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 8406 | |
| Decimal Number | 3649 | |
| Uppercase Letter | 2260 | 11.2% |
| Space Separator | 1499 | 7.4% |
| Currency Symbol | 1484 | 7.3% |
| Dash Punctuation | 742 | 3.7% |
| Other Punctuation | 742 | 3.7% |
| Close Punctuation | 725 | 3.6% |
| Open Punctuation | 725 | 3.6% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| s | 2151 | |
| o | 1496 | |
| r | 824 | 9.8% |
| e | 795 | 9.5% |
| l | 759 | 9.0% |
| a | 747 | 8.9% |
| d | 747 | 8.9% |
| t | 725 | 8.6% |
| y | 46 | 0.5% |
| p | 29 | 0.3% |
| Other values (4) | 87 | 1.0% |
Decimal Number
| Value | Count | Frequency (%) |
| 1 | 919 | |
| 2 | 356 | 9.8% |
| 0 | 328 | 9.0% |
| 6 | 317 | 8.7% |
| 4 | 304 | 8.3% |
| 5 | 293 | 8.0% |
| 9 | 285 | 7.8% |
| 8 | 285 | 7.8% |
| 3 | 283 | 7.8% |
| 7 | 279 | 7.6% |
Uppercase Letter
| Value | Count | Frequency (%) |
| K | 1436 | |
| G | 713 | |
| P | 41 | 1.8% |
| E | 29 | 1.3% |
| H | 24 | 1.1% |
| S | 17 | 0.8% |
Other Punctuation
| Value | Count | Frequency (%) |
| . | 725 | |
| : | 17 | 2.3% |
Space Separator
| Value | Count | Frequency (%) |
| 1499 |
Currency Symbol
| Value | Count | Frequency (%) |
| $ | 1484 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 742 |
Close Punctuation
| Value | Count | Frequency (%) |
| ) | 725 |
Open Punctuation
| Value | Count | Frequency (%) |
| ( | 725 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 10666 | |
| Common | 9566 |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| s | 2151 | |
| o | 1496 | |
| K | 1436 | |
| r | 824 | 7.7% |
| e | 795 | 7.5% |
| l | 759 | 7.1% |
| a | 747 | 7.0% |
| d | 747 | 7.0% |
| t | 725 | 6.8% |
| G | 713 | 6.7% |
| Other values (10) | 273 | 2.6% |
Common
| Value | Count | Frequency (%) |
| 1499 | ||
| $ | 1484 | |
| 1 | 919 | |
| - | 742 | |
| ) | 725 | |
| . | 725 | |
| ( | 725 | |
| 2 | 356 | 3.7% |
| 0 | 328 | 3.4% |
| 6 | 317 | 3.3% |
| Other values (7) | 1746 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 20232 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| s | 2151 | 10.6% |
| 1499 | 7.4% | |
| o | 1496 | 7.4% |
| $ | 1484 | 7.3% |
| K | 1436 | 7.1% |
| 1 | 919 | 4.5% |
| r | 824 | 4.1% |
| e | 795 | 3.9% |
| l | 759 | 3.8% |
| a | 747 | 3.7% |
| Other values (27) | 8122 |
| Distinct | 463 |
|---|---|
| Distinct (%) | 62.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| Description Medical Laboratory Scientist - Texas Health Huguley- operated as joint venture between Texas Health Resources and AdventHealth Location Address: 11801 South Fwy., Burleson, TX 76028 Top Reasons to Work At Texas Health Huguley, Burleson, TX Our care for patients extend to the spiritual level by praying with patients and families and providing on call, 24 hours, 7 days a week Chaplains for spiritual support. Award winning facility and departments including Great Place to Work by Beckers Hospital Review and Gallup. Work with the latest technology and top experts including Daisy Award recipients while on the way to Magnet status2020. Amazing medical benefits through Aetna plus an onsite full-service fitness center. Growth opportunities designed for each employee. Located about 10 minutes from downtown Fort Worth and near TCU in the award-winning school district, Burleson ISD which also provides a low-cost of living. Work Hours/Shift: Full Time 3rd Shift You Will Be Responsible For: Accurately performs and expeditiously reports laboratory tests, according to departmental policies, CLIA law and regulatory standards. Determines identity and suitability of specimens when received, according to procedure policies. Assures test accuracy by performing and recording control testing, in accordance with departmental policy on each shift, as observed by the department supervisor. Performs tests in accordance with department policy and reports results in a timely manner, as documented in test records. Organizes daily work efficiently and expedites testing as observed by supervisor. Investigates problems and takes initiative in resolving them, both technical and non-technical, and communicates the outcome to the appropriate individual. Reviews integrity of report by checking legibility, completeness and credibility of results prior to releasing, as observed by supervisor. Accurately performs, records, transcribes or reviews proficiency testing material by the stated deadline, according to laboratory policy and CLIA law, as evidenced in the survey evaluation results. Maintains instrumentation and department supplies in order to ensure efficient departmental operations. Performs and documents assigned equipment maintenance duties per shift according to maintenance manuals. Restocks department supplies on a regular basis to ensure adequacy of inventory, as documented on department checklist. Recognizes problems in instrumentation, performs first line repairs as outlined in procedure manual and notifies appropriate individual if unable to adequately alleviate the problem. Promotes and contributes positively to intradepartmental and interdepartmental communications to ensure efficient departmental operation. Answers telephone and pages promptly and in a courteous manner, identifying self and department at all times. Assists in orientation and training of new associates unfamiliar with the department, as observed by the supervisor. Relays all appropriate information during shift hand-off, and ensures department is covered prior to leaving. Demonstrates good judgment in directing phone calls or questions to the appropriate department or individual. Promotes a safe working environment by regimented clean up and adherence to safety manuals. Adheres to established departmental guidelines for clean up on each shift. Protects self and co-workers by practicing safety precautions as established in safety and infection control manuals. Maintains appropriate departmental records and filing systems to ensure the expeditious retrieval of information and to comply with regulatory requirements. Ensures that results are properly filed and that data is recorded in a legible manner as evidenced in departmental records. Exhibits ability to perform essential computer operations pertaining to job duties. Performs outpatient ordering as defined in the department policy. Maintains a professional attitude in the work place regarding procedures, personnel and continuing education. Demonstrates a willingness to learn new procedures and instrumentation by attending training sessions and becoming familiar with new revisions as they occur. Reviews policies frequently, assists in updating contents and complies with procedures contained therein. Demonstrates flexibility by being able to work various departments and alternative shifts when requested. Exhibits willingness to help associates in their tasks when able. Accepts constructive criticism and feedback. Maintains necessary continuing education credits, keeps required competencies current, and maintains valid ASCP Certification. Qualifications What You Will Need: Must have a B.S. degree in Medical Technology, Medical Laboratory Science or a related science. Valid ASCP or AMT Technologist/Scientist Certification. At least one year of experience is preferred. Job Summary: Perform and report clinical laboratory analysis to assist physicians and other hospital staff in the diagnosis and monitoring of patients. This facility is an equal opportunity employer and complies with federal, state and local anti-discrimination laws, regulations and ordinances. | 4 |
|---|---|
| Palermo Villa Inc. is interested in a high-energy, poised and confident individual to assist in the development of concepts, products and optimization projects through Palermo's vigorous consumer-driven R&D process. The position will apply scientific and culinary principles in research and development. Develops the understanding of and ability to translate food trends into innovative opportunities, stimulate new food ideas and product concepts. Identify, evaluate and develop potential new product development opportunities. From bench-top samples to commercialized products and finished product specifications Assist in food product formulation from bench top to commercialization using a continuously developing skill set in food formulation and processing equipment capability understanding. Applies an analytical approach to the solution of a wide variety of problems and assimilates the details and significance of various scientific analyses, procedures, and tests Demonstrates initiative, creativity and thoroughness in the execution of complex projects Plans and conducts independent research projects and participates in the development of project objectives Contributes to the development of project strategies and recommends technical direction to management Evaluates technical trends in their specific area of expertise or assignment and makes recommendations for process or product improvements and identify opportunities for new or improved process or products Organize and direct sample development for sales presentations, consumer testing and food safety assurance Maintains written technical documentation and product and process specifications as pertaining to R&D Utilizes or directs internal (manufacturing, engineering, marketing, quality systems, procurement) and external (suppliers, consultants) functional experts to resolve issues. Assist in PR events, food shows and Sales presentations on key customer calls Provide technical support/serves as product development contact for Sales, Customer and Operations To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The position requires 5+ years' experience developing products within the food industry. Strong interpersonal and communication skills Ability to effectively present information to top management, public groups, and/or boards of directors. Ability to apply mathematical operations to such tasks as frequency distribution, determination of test reliability and validity, analysis of variance, correlation techniques, sampling theory, and factor analysis. Ability to define problems, collect data, establish facts, and draw valid conclusions. Strong computer skills are necessary Educational Requirements: Bachelor's Degree in Food Science, Biology, Chemistry, Culinary or equivalent | 4 |
| Under direct supervision of the Director of Database Marketing, the Marketing Data Analyst will work closely with members of the database marketing team and the FP&A marketing analysis team to derive insights from large amounts of customer and transactional data to develop segmentations, strategies, visualizations, reports, and recommendations for various marketing purposes. The Marketing Data Analyst will assist management with the interpretation, evaluation and interrelationship of data and generate integrated business analysis and projections to facilitate decision making. Essential Duties & Responsibilities Develop queries in SAS that create marketing campaigns to optimize profit and produce multi-channel campaign outputs. Design and evaluate various tests and optimizations of campaigns. Monitor the quality of all data at both the project and output level for the Database Marketing team. Support the integration of new data sources and analyze and confirm the overall quality and integrity of source data. Generate database extracts for Database Marketing teams as needed. Provide campaign analytics to extended team, including insights and recommendations to improve message effectiveness and campaign performance. Build business intelligence, reports and dashboards using software like SAS, Microsoft Excel/VBA, Tableau, or SAS Visual Analytics that include segment/campaign profitability and customer behavior or trends. Create relationships with internal stakeholders to discover how data, platform and tools can assist to execute business needs. Identify new business opportunities or potential risks based on data analysis on subject matters of various operations departments. Train users as needed. Perform other duties as assigned to support the efficient operation of the department. Education/Experience/Qualifications Bachelors or Masters Degree in Computer Science, Economics, Marketing, Finance, Mathematics, or related field required. 2+ years of experience with SAS and/or SQL and analyzing large datasets. Equivalent combination of education and progressive, relevant and direct experience may be considered in lieu of minimum educational/experience requirements indicated above. Advanced proficiency in Microsoft Excel and Word. Experience working with relational databases is required. Experience in programming/scripting. Experience with data visualization, reporting & dash boarding tools such as SAS visual Analytics or Tableau. Experience with Google Analytics custom reports and dashboards preferred. Familiarity with marketing methodologies and systems such as segmentation modeling, targeting, CRM, and ROI projections and evaluation. Predictive Modeling experience preferred. Employee must have experience demonstrating the utmost discretion and confidentiality as they will have access to confidential information including, but not limited to: customer contact information, customer financial data, and organizational financial data. Excellent communication skills, both written and verbal. Must be able to obtain/maintain any necessary certifications and/or licenses. Ability to mentor coordinators and administrative staff. Ability to work with mathematical concepts such as probability and statistical inference. Ability to apply concepts such as fractions, percentages, ratios, and proportions to practical situations, including the development of financial statistical models and forecasts. Ability to define problems, collect data, establish facts, and draw valid conclusions with minimal direction. Ability to interpret an extensive variety of technical instructions in mathematical or diagram form and deal with several abstract and concrete variables. Ability to effectively present information to, and respond to questions from, groups of managers and directors. Ability to read, analyze, and interpret general business periodicals, professional journals, technical procedures, governmental regulations, financial reports, and legal documents. Ability to respond to common inquiries or complaints from customers, regulatory agencies, or members of the business community. Certificates/Licenses/Registrations At the discretion of the San Manuel Tribal Gaming Commission you may be required to obtain and maintain a gaming license. San Manuel Band of Mission Indians and San Manuel Casino will make reasonable accommodations in compliance with the Americans with Disabilities Act of 1990. As one of the largest private employers in the Inland Empire, San Manuel deeply cares about the future, growth and well-being of its employees. Join our team today! | 4 |
| Responsibilities Include but may not be limited to: performing various tasks assisting in development of new items, renovation of existing formulations, and supports efforts to ensure quality product is produced, maintained, and documented. Additionally, this position will underwrite efforts in product development and distribution by our sales and prourement teams. This position will also be responsible for maintaining and entering data in several databases. As a member of the R&D Team, you will help develop products which can be reproduced in a large-scale food manufacturing environment. Assist in the development of new bean products from concept approval, formulation, product development, plant trial runs to launch and post-launch review by collaborating with Marketing , Sales, Project management, QA, and Production. Participate as an active member of cross-functional business development teams comprosed of individuals from a variety of desciplines, includjing Marketing, Finance, Purchasing and many others. Assist in redesign & renovation of existing products to increase quality, reduce costs, and/or increase production efficiencies. Partner internally and extenally to source new ingredients and leverage vendor expertise in ingredient functionality. Assist Quality and Procurement departments in maintaining specifications for new ingredients and/or suppliers. Supoport production with troubleshooting out of spec product or production concerns on established products. Maintain accurate product records, documentation and archives in various databases including global data synchronization of existing retail business. Maintain laboratory, including upkeep of equipment, stocking of supplies, and general cleaning of work areas. Performs other related and assigned duties as necessary. Minimun Qualifications Must hold a Bachelors degree in Food Science from an accredited University. Previous experience in food product development & food manufacturing strongly preferred. Ability and interest to work in laboratory, pilot plant and manufacturing scale environments. Proven ability to manage multiple assignments/tasks. Ability to work independently while collaborating and communicating with team members in various departments. Strong communication skills (oral and written). Knowlege of Genesis labeling system preferred but not required. Must be physically capable of lifting 50lbs. weight restriction. | 4 |
| What We Do: At the SEI Emerging Technology Center, we describe our work as “making the recently possible mission-practical.” We help our government customers stay at the leading edge of technology by identifying, demonstrating, extending, and applying emerging software technologies to solve real government problems. We currently work in the fields of human-machine interaction, applied artificial intelligence and machine learning, and advanced computing—areas that are changing and progressing rapidly. As we show our customers how new technologies can improve their mission capabilities through rapid prototyping and iterative development, we both rely on and shape academic and industrial research. Are you creative, curious, energetic, collaborative, technology-focused, and hard-working? Are you interested in making a difference by bringing innovation to government organizations and beyond? Apply to join our team. Position Summary: As a senior research scientist focusing on machine learning, you will identify, shape, apply, conduct, and lead research that matches critical U.S. government needs. Requirements: BS in Computer Science or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years of experience; OR PhD with five (5) years of experience. Flexible to travel to other SEI offices in Pittsburgh and Washington, DC, sponsor sites, conferences, and offsite meetings on occasion. Moderate (25") travel outside of your home location. You will be subject to a background investigation and must be eligible to obtain and maintain a Department of Defense security clearance. Duties: Hands-on research: You’ll conduct and lead novel research in applied machine learning and artificial intelligence. Solution development: You’ll work with and lead interdisciplinary teams to turn research results into prototype operational capabilities for government customers and stakeholders. Strategy: You’ll work with Center leaders and colleagues to plan, develop, and carry out an overall research strategy, and to influence the national research agenda regarding future technology. Collaboration: You'll actively participate on teams of software developers, researchers, designers, and technical leads. You'll build relationships and collaborate with researchers, government customers, and other stakeholders to understand challenges, needs, possible solutions, and research directions. Mentoring: You'll contribute to improving the overall technical capabilities of the Center by mentoring and teaching others, participating in design (software and otherwise) sessions, and sharing insights and wisdom across the SEI Emerging Technology Center team. Knowledge, Skills, and Abilities: Deep technical knowledge: You have performed extensive research in applied machine learning and artificial intelligence. You have worked with tools, techniques, algorithms, software, and programming languages for deep learning, reinforcement learning, statistics, sensors and sensor fusion, planning, computer vision, or related areas. Communication and Collaboration: You have strong written and verbal communication skills and can interact collaboratively and diplomatically with customers and colleagues. You grasp the big picture, direction, and goals of an effort while focusing great attention to detail. You can present complex ideas to people who may not have a deep understanding of the subject area. Dedication: You can meet deadlines while multi-tasking–sometimes under pressure and with shifting priorities. Creativity and Innovation: You are creative and curious, and you are inspired by the prospect of collaborating with premier researchers and visionaries at Carnegie Mellon and other universities and organizations. You quickly learn new procedures, techniques, and approaches. You are forward-looking and can connect research with practical challenges. Knowledge and Learning: You possess broad technical interests along with a deep knowledge of a particular field such as human-computer interaction, data analytics and machine learning, advanced computing, and autonomy and adaptive systems. Desired Experience: Research practices and publications: You have a track record of conducting research in machine learning and artificial intelligence. You have a reputation for the highest level of research and technical integrity. You have demonstrated contributions and have published research. Familiarity with emerging trends and opportunities: You are familiar with technical challenges and emerging trends in computing and information science, and you are aware of opportunities in industry and government. Technical leadership: You have led research projects and have experience collaborating across research teams and mentoring other researchers. Proposals: You have formulated and delivered successful research proposals to funding agencies and led the resulting projects. Government projects: You have worked or are familiar with DARPA, IARPA, Service Labs, or other government research sponsors More Information Please visit “Why Carnegie Mellon” to learn more about becoming part of an institution inspiring innovations that change the world. A listing of employee benefits is available at: www.cmu.edu/jobs/benefits-at-a-glance/. Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran. | 4 |
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| 1st row | Data Scientist Location: Albuquerque, NM Education Required: Bachelor’s degree required, preferably in math, engineering, business, or the sciences. Skills Required: Bachelor’s Degree in relevant field, e.g., math, data analysis, database, computer science, Artificial Intelligence (AI); three years’ experience credit for Master’s degree; five years’ experience credit for a Ph.D Applicant should be proficient in the use of Power BI, Tableau, Python, MATLAB, Microsoft Word, PowerPoint, Excel, and working knowledge of MS Access, LMS, SAS, data visualization tools, and have a strong algorithmic aptitude Excellent verbal and written communication skills, and quantitative analytical skills are required Applicant must be able to work in a team environment U.S. citizenship and ability to obtain a DoD Secret Clearance required Responsibilities: The applicant will be responsible for formulating analytical solutions to complex data problems; creating data analytic models to improve data metrics; analyzing customer behavior and trends; delivering insights to stakeholders, as well as designing and crafting reports, dashboards, models, and algorithms to make data insights actionable; selecting features, building and optimizing classifiers using machine learning techniques; data mining using state-of-the-art methods, extending organization’s data with third party sources of information when needed; enhancing data collection procedures to include information that is relevant for building analytic systems; processing, cleansing, and verifying the integrity of data used for analysis; doing ad-hoc analysis and presenting results in a clear manner; and creating automated anomaly detection systems and constant tracking of its performance. Benefits: We offer competitive salaries commensurate with education and experience. We have an excellent benefits package that includes: Comprehensive health, dental, life, long and short term disability insurance 100% Company funded Retirement Plans Generous vacation, holiday and sick pay plans Tuition assistance Benefits are provided to employees regularly working a minimum of 30 hours per week. Tecolote Research is a private, employee-owned corporation where people are our primary resource. Our investments in technology and training give our employees the tools to ensure our clients are provided the solutions they need, and our very high employee retention rate and stable workforce is an added value to our customers. Apply now to connect with a company that invests in you. |
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| 2nd row | What You Will Do: I. General Summary The Healthcare Data Scientist position will join our Advanced Analytics group at the University of Maryland Medical System (UMMS) in support of its strategic priority to become a data-driven and outcomes-oriented organization. The successful candidate will have 3+ years of experience with Machine Learning, Predictive Modeling, Statistical Analysis, Mathematical Optimization, Algorithm Development and a passion for working with healthcare data. Previous experience with various computational approaches along with an ability to demonstrate a portfolio of relevant prior projects is essential. This position will report to the UMMS Vice President for Enterprise Data and Analytics (ED&A). II. Principal Responsibilities and Tasks • Develops predictive and prescriptive analytic models in support of the organization’s clinical, operations and business initiatives and priorities. • Deploys solutions so that they provide actionable insights to the organization and are embedded or integrated with application systems • Supports and drives analytic efforts designed around organization’s strategic priorities and clinical/business problems • Works in a team to drive disruptive innovation, which may translate into improved quality of care, clinical outcomes, reduced costs, temporal efficiencies and process improvements. • Builds and extends our analytics portfolio supported by robust documentation • Works with autonomy to find solutions to complex problems using open source tools and in-house development • Stays abreast of state-of-the-art literature in the fields of operations research, statistical modeling, statistical process control and mathematical optimization • Creates, communicates, and manages the project plans and other required project documentation and provides updates to leadership as necessary • Develops and maintains relationships with business, IT and clinical leaders and stakeholders across the enterprise to facilitate collaboration and effective communication • Works with the analytics team and clinical/business stakeholders to develop pilots so that they may be tested and validated in pilot settings • Performs analysis to evaluate primary and secondary objectives from such pilots • Assists leadership with strategies for scaling successful projects across the organization and enhances the analytics applications based on feedback from end-users and clinical/business consumers • Assists leadership with dissemination of success stories (and failures) in an effort to increase analytics literacy and adoption across the organization. What You Need to Be Successful: III. Education and Experience • Master’s or higher degree (may be substituted by relevant work experience) in applied mathematics, physics, computer science, engineering, statistics or a related field • 3+ years of Mathematical Optimization, Machine Learning, Predictive Analytics and Algorithm Development experience (experience with tools such as WEKA, RapidMiner, R. Python or other open source tools strongly desired) • Strong development skills in two or more of the following: C/C++, C#, Python, Java • Combining analytic methods with advanced data visualizations • Expert ability to breakdown and clearly define problems • Experience with Natural Language Processing preferred IV. Knowledge, Skills and Abilities • Proven communications skills – Effective at working independently and in collaboration with other staff members. Capable of clearly presenting findings orally, in writing, or through graphics. • Proven analytical skills – Able to compare, contrast, and validate work with keen attention to detail. Skilled in working with “real world” data including scrubbing, transformation, and imputation. • Proven problem solving skills – Able to plan work, set clear direction, and coordinate own tasks in a fast-paced multidisciplinary environment. Expert at triaging issues, identifying data anomalies, and debugging software. • Design and prototype new application functionality for our products. • Change oriented – actively generates process improvements; supports and drives change, and confronts difficult circumstances in creative ways • Effective communicator and change agent • Ability to prioritize the tasks of the project timeline to achieve the desired results • Strong analytic and problem solving skills • Ability to cooperatively and effectively work with people from various organization levels We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. |
| 3rd row | KnowBe4, Inc. is a high growth information security company. We are the world's largest provider of new-school security awareness training and simulated phishing. KnowBe4 was created to help organizations manage the ongoing problem of social engineering. Tens of thousands of organizations worldwide use KnowBe4's platform to mobilize their end users as a last line of defense and enable them to make better security decisions, every day. We are ranked #1 best place to work in technology nationwide by Fortune Magazine and have placed #1 or #2 in The Tampa Bay Top Workplaces Survey for the last four years. We also just had our 27th record-setting quarter in a row! The Data Scientist will work closely with the VP of FP&A and the Quantitative Analytics Manager to implement advanced analytical models and other data-driven solutions. Responsibilities: Work with key stakeholders throughout the organization to identify opportunities using financial data to develop business solutions. Develop new and enhance existing data collection procedures to ensure that all data relevant for analyses is captured. Cleanse, consolidate, and verify the integrity of data used in analyses. Build and validate predictive models to increase customer retention, revenue generation, and other business outcomes. Develop relevant statistical models to assist with profitability forecasting Create the analytics to leverage known, inferred and appended information about origins and recognizing patterns to assist in outlook forecasting Assist in the design and data modeling of data warehouse. Visualize data, especially in reports and dashboards, to communicate analysis results to stakeholders. Extend data collection to unstructured data within the company and external sources Mine and collect data (both structured and unstructured) to detect patterns, opportunities and insights that drive our organization Create and execute automation and data mining requests utilizing SQL, Access, Excel, SAS and other statistical programs Trouble shoot forecast and optimization anomalies with FP&A team through the use of statistical and mathematical optimization models. Develop testing to explain and or reduce these anomalies. Oversee and develop key metric forecasts as well as provide budget support based on trends in the business/industry. Minimum Qualifications: Master's degree in Statistics, Computer Science, Mathematics or other quantitative discipline required 2-3 years of experience in similar role (Master's Degree) 0-2 years of experience in similar role (PhD) Experience leveraging predictive modeling, big data analytics, exploratory data analysis and machine learning to drive significant business impact Experience with statistical computer languages (Python, R etc.) to manipulate and analyze large datasets preferred. Experience with data visualization tools like D3.js, matplotlib, etc., preferred Exceptional understanding of machine learning algorithms such as Random Forest, SVM, k-NN, Naïve Bayes, Gradient Boosting a plus. Applied statistical skills including statistical testing, regression, etc. Experience with data bases, query languages, and associated data architecture. Experience with distributed computing tools (Hive, Spark, etc.) is a plus. Strong analytical skills and ability to meet project deadlines. Note: An applicant assessment, background check and drug test may be part of your hiring procedure. No recruitment agencies, please. |
| 4th row | *Organization and Job ID** Job ID: 310709 Directorate: Earth & Biological Sciences Division: Biological Sciences Group: Exposure Science Team *Job Description** The Biological System Science (BSS) Group in the Biological Sciences Division of the Pacific Northwest National Laboratory (PNNL) is seeking a staff scientist with multidisciplinary experience in computational chemistry, cheminformatics, advanced statistics and/or machine learning/deep learning/AI. Preferred candidates will have a broad understanding of the state of computational metabolomics and experience in designing and implementing novel deep learning networks for chemistry applications. Research experience in drug design, cheminformatics, deep learning, machine learning and/or small molecule identification is also highly valued. Successful candidates will join a large, uniquely collaborative, collegial group of innovators driving the integration of data science, computational science and analytical chemistry to solve the nations most challenging problems in human health, chemical forensics, and national security. The BSS Group is diverse and inclusive, working closely with colleagues across the laboratory with expertise in computational biology, integrative omics, applied mathematics, computer science, and statistics. + Apply knowledge of statistics, machine learning, advanced mathematics, simulation, software development, and data modeling to to design, development and implement methods that integrate, clean and analyze data, recognize patterns, address uncertainty, pose questions, and make discoveries from structured and/or unstructured data. + Produce solutions driven by exploratory data analysis from complex and high-dimensional datasets. + Design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from data. + Develop and maintain existing deep learning networks that generate novel molecules for drug discovery applications + Contribue or author proposals, peer-reviewed papers, and other technical products. *Minimum Qualifications** BS/BA with 0-1 years of experience or MS/MA with 0-1 years of experience *Preferred Qualifications** + MS in chemical engineering, computer science, or related field with a GPA of 3.5+ 5+ years of research experience + Intermediate level programming experience (preferably Python) and high-performance computing experience + At least one first author published, or proof of submitted, paper applying deep learning for use in novel compound generation + Understanding of the NMDA receptor and potential drug targets + Research experience in drug design, cheminformatics, deep learning, machine learning and/or small molecule identification *Equal Employment Opportunity** Battelle Memorial Institute (BMI) at Pacific Northwest National Laboratory (PNNL) is an Affirmative Action/Equal Opportunity Employer and supports diversity in the workplace. All employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, marital or family status, sexual orientation, gender identity, or genetic information. All BMI staff must be able to demonstrate the legal right to work in the United States. BMI is an E-Verify employer. Learn more at jobs.pnnl.gov. *_Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from participation in certain foreign government talent recruitment programs. If you are offered a position at PNNL and are currently a participant in a foreign government talent recruitment program you will be required to disclose this information before your first day of employment._** _Directorate:_ _Earth & Biological Sciences_ _Job Category:_ _Scientists/Scientific Support_ _Group:_ _Biological Systems Science_ _Opening Date:_ _2020-03-26_ _Closing Date:_ _2020-04-05_ |
| 5th row | Data Scientist Affinity Solutions / Marketing Cloud seeks smart, curious, technically savvy candidates to join our cutting-edge data science team. We hire the best and brightest and give them the opportunity to work on industry-leading technologies. The data sciences team at AFS/Marketing Cloud build models, machine learning algorithms that power all our ad-tech/mar-tech products at scale, develop methodology and tools to precisely and effectively measure market campaign effects, and research in-house and public data sources for consumer spend behavior insights. In this role, you'll have the opportunity to come up with new ideas and solutions that will lead to improvement of our ability to target the right audience, derive insights and provide better measurement methodology for marketing campaigns. You'll access our core data asset and machine learning infrastructure to power your ideas. Duties and Responsibilities · Support all clients model building needs, including maintaining and improving current modeling/scoring methodology and processes, · Provide innovative solutions to customized modeling/scoring/targeting with appropriate ML/statistical tools, · Provide analytical/statistical support such as marketing test design, projection, campaign measurement, market insights to clients and stakeholders. · Mine large consumer datasets in the cloud environment to support ad hoc business and statistical analysis, · Develop and Improve automation capabilities to enable customized delivery of the analytical products to clients, · Communicate the methodologies and the results to the management, clients and none technical stakeholders. Basic Qualifications · Advanced degree in Statistics/Mathematics/Computer Science/Economics or other fields that requires advanced training in data analytics. · Being able to apply basic statistical/ML concepts and reasoning to address and solve business problems such as targeting, test design, KPI projection and performance measurement. · Entrepreneurial, highly self-motivated, collaborative, keen attention to detail, willingness and capable learn quickly, and ability to effectively prioritize and execute tasks in a high pressure environment. · Being flexible to accept different task assignments and able to work on a tight time schedule. · Excellent command of one or more programming languages; preferably Python, SAS or R · Familiar with one of the database technologies such as PostgreSQL, MySQL, can write basic SQL queries · Great communication skills (verbal, written and presentation) Preferred Qualifications · Experience or exposure to large consumer and/or demographic data sets. · Familiarity with data manipulation and cleaning routines and techniques. |
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| Description Medical Laboratory Scientist - Texas Health Huguley- operated as joint venture between Texas Health Resources and AdventHealth Location Address: 11801 South Fwy., Burleson, TX 76028 Top Reasons to Work At Texas Health Huguley, Burleson, TX Our care for patients extend to the spiritual level by praying with patients and families and providing on call, 24 hours, 7 days a week Chaplains for spiritual support. Award winning facility and departments including Great Place to Work by Beckers Hospital Review and Gallup. Work with the latest technology and top experts including Daisy Award recipients while on the way to Magnet status2020. Amazing medical benefits through Aetna plus an onsite full-service fitness center. Growth opportunities designed for each employee. Located about 10 minutes from downtown Fort Worth and near TCU in the award-winning school district, Burleson ISD which also provides a low-cost of living. Work Hours/Shift: Full Time 3rd Shift You Will Be Responsible For: Accurately performs and expeditiously reports laboratory tests, according to departmental policies, CLIA law and regulatory standards. Determines identity and suitability of specimens when received, according to procedure policies. Assures test accuracy by performing and recording control testing, in accordance with departmental policy on each shift, as observed by the department supervisor. Performs tests in accordance with department policy and reports results in a timely manner, as documented in test records. Organizes daily work efficiently and expedites testing as observed by supervisor. Investigates problems and takes initiative in resolving them, both technical and non-technical, and communicates the outcome to the appropriate individual. Reviews integrity of report by checking legibility, completeness and credibility of results prior to releasing, as observed by supervisor. Accurately performs, records, transcribes or reviews proficiency testing material by the stated deadline, according to laboratory policy and CLIA law, as evidenced in the survey evaluation results. Maintains instrumentation and department supplies in order to ensure efficient departmental operations. Performs and documents assigned equipment maintenance duties per shift according to maintenance manuals. Restocks department supplies on a regular basis to ensure adequacy of inventory, as documented on department checklist. Recognizes problems in instrumentation, performs first line repairs as outlined in procedure manual and notifies appropriate individual if unable to adequately alleviate the problem. Promotes and contributes positively to intradepartmental and interdepartmental communications to ensure efficient departmental operation. Answers telephone and pages promptly and in a courteous manner, identifying self and department at all times. Assists in orientation and training of new associates unfamiliar with the department, as observed by the supervisor. Relays all appropriate information during shift hand-off, and ensures department is covered prior to leaving. Demonstrates good judgment in directing phone calls or questions to the appropriate department or individual. Promotes a safe working environment by regimented clean up and adherence to safety manuals. Adheres to established departmental guidelines for clean up on each shift. Protects self and co-workers by practicing safety precautions as established in safety and infection control manuals. Maintains appropriate departmental records and filing systems to ensure the expeditious retrieval of information and to comply with regulatory requirements. Ensures that results are properly filed and that data is recorded in a legible manner as evidenced in departmental records. Exhibits ability to perform essential computer operations pertaining to job duties. Performs outpatient ordering as defined in the department policy. Maintains a professional attitude in the work place regarding procedures, personnel and continuing education. Demonstrates a willingness to learn new procedures and instrumentation by attending training sessions and becoming familiar with new revisions as they occur. Reviews policies frequently, assists in updating contents and complies with procedures contained therein. Demonstrates flexibility by being able to work various departments and alternative shifts when requested. Exhibits willingness to help associates in their tasks when able. Accepts constructive criticism and feedback. Maintains necessary continuing education credits, keeps required competencies current, and maintains valid ASCP Certification. Qualifications What You Will Need: Must have a B.S. degree in Medical Technology, Medical Laboratory Science or a related science. Valid ASCP or AMT Technologist/Scientist Certification. At least one year of experience is preferred. Job Summary: Perform and report clinical laboratory analysis to assist physicians and other hospital staff in the diagnosis and monitoring of patients. This facility is an equal opportunity employer and complies with federal, state and local anti-discrimination laws, regulations and ordinances. | 4 | 0.5% |
| Palermo Villa Inc. is interested in a high-energy, poised and confident individual to assist in the development of concepts, products and optimization projects through Palermo's vigorous consumer-driven R&D process. The position will apply scientific and culinary principles in research and development. Develops the understanding of and ability to translate food trends into innovative opportunities, stimulate new food ideas and product concepts. Identify, evaluate and develop potential new product development opportunities. From bench-top samples to commercialized products and finished product specifications Assist in food product formulation from bench top to commercialization using a continuously developing skill set in food formulation and processing equipment capability understanding. Applies an analytical approach to the solution of a wide variety of problems and assimilates the details and significance of various scientific analyses, procedures, and tests Demonstrates initiative, creativity and thoroughness in the execution of complex projects Plans and conducts independent research projects and participates in the development of project objectives Contributes to the development of project strategies and recommends technical direction to management Evaluates technical trends in their specific area of expertise or assignment and makes recommendations for process or product improvements and identify opportunities for new or improved process or products Organize and direct sample development for sales presentations, consumer testing and food safety assurance Maintains written technical documentation and product and process specifications as pertaining to R&D Utilizes or directs internal (manufacturing, engineering, marketing, quality systems, procurement) and external (suppliers, consultants) functional experts to resolve issues. Assist in PR events, food shows and Sales presentations on key customer calls Provide technical support/serves as product development contact for Sales, Customer and Operations To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The position requires 5+ years' experience developing products within the food industry. Strong interpersonal and communication skills Ability to effectively present information to top management, public groups, and/or boards of directors. Ability to apply mathematical operations to such tasks as frequency distribution, determination of test reliability and validity, analysis of variance, correlation techniques, sampling theory, and factor analysis. Ability to define problems, collect data, establish facts, and draw valid conclusions. Strong computer skills are necessary Educational Requirements: Bachelor's Degree in Food Science, Biology, Chemistry, Culinary or equivalent | 4 | 0.5% |
| Under direct supervision of the Director of Database Marketing, the Marketing Data Analyst will work closely with members of the database marketing team and the FP&A marketing analysis team to derive insights from large amounts of customer and transactional data to develop segmentations, strategies, visualizations, reports, and recommendations for various marketing purposes. The Marketing Data Analyst will assist management with the interpretation, evaluation and interrelationship of data and generate integrated business analysis and projections to facilitate decision making. Essential Duties & Responsibilities Develop queries in SAS that create marketing campaigns to optimize profit and produce multi-channel campaign outputs. Design and evaluate various tests and optimizations of campaigns. Monitor the quality of all data at both the project and output level for the Database Marketing team. Support the integration of new data sources and analyze and confirm the overall quality and integrity of source data. Generate database extracts for Database Marketing teams as needed. Provide campaign analytics to extended team, including insights and recommendations to improve message effectiveness and campaign performance. Build business intelligence, reports and dashboards using software like SAS, Microsoft Excel/VBA, Tableau, or SAS Visual Analytics that include segment/campaign profitability and customer behavior or trends. Create relationships with internal stakeholders to discover how data, platform and tools can assist to execute business needs. Identify new business opportunities or potential risks based on data analysis on subject matters of various operations departments. Train users as needed. Perform other duties as assigned to support the efficient operation of the department. Education/Experience/Qualifications Bachelors or Masters Degree in Computer Science, Economics, Marketing, Finance, Mathematics, or related field required. 2+ years of experience with SAS and/or SQL and analyzing large datasets. Equivalent combination of education and progressive, relevant and direct experience may be considered in lieu of minimum educational/experience requirements indicated above. Advanced proficiency in Microsoft Excel and Word. Experience working with relational databases is required. Experience in programming/scripting. Experience with data visualization, reporting & dash boarding tools such as SAS visual Analytics or Tableau. Experience with Google Analytics custom reports and dashboards preferred. Familiarity with marketing methodologies and systems such as segmentation modeling, targeting, CRM, and ROI projections and evaluation. Predictive Modeling experience preferred. Employee must have experience demonstrating the utmost discretion and confidentiality as they will have access to confidential information including, but not limited to: customer contact information, customer financial data, and organizational financial data. Excellent communication skills, both written and verbal. Must be able to obtain/maintain any necessary certifications and/or licenses. Ability to mentor coordinators and administrative staff. Ability to work with mathematical concepts such as probability and statistical inference. Ability to apply concepts such as fractions, percentages, ratios, and proportions to practical situations, including the development of financial statistical models and forecasts. Ability to define problems, collect data, establish facts, and draw valid conclusions with minimal direction. Ability to interpret an extensive variety of technical instructions in mathematical or diagram form and deal with several abstract and concrete variables. Ability to effectively present information to, and respond to questions from, groups of managers and directors. Ability to read, analyze, and interpret general business periodicals, professional journals, technical procedures, governmental regulations, financial reports, and legal documents. Ability to respond to common inquiries or complaints from customers, regulatory agencies, or members of the business community. Certificates/Licenses/Registrations At the discretion of the San Manuel Tribal Gaming Commission you may be required to obtain and maintain a gaming license. San Manuel Band of Mission Indians and San Manuel Casino will make reasonable accommodations in compliance with the Americans with Disabilities Act of 1990. As one of the largest private employers in the Inland Empire, San Manuel deeply cares about the future, growth and well-being of its employees. Join our team today! | 4 | 0.5% |
| Responsibilities Include but may not be limited to: performing various tasks assisting in development of new items, renovation of existing formulations, and supports efforts to ensure quality product is produced, maintained, and documented. Additionally, this position will underwrite efforts in product development and distribution by our sales and prourement teams. This position will also be responsible for maintaining and entering data in several databases. As a member of the R&D Team, you will help develop products which can be reproduced in a large-scale food manufacturing environment. Assist in the development of new bean products from concept approval, formulation, product development, plant trial runs to launch and post-launch review by collaborating with Marketing , Sales, Project management, QA, and Production. Participate as an active member of cross-functional business development teams comprosed of individuals from a variety of desciplines, includjing Marketing, Finance, Purchasing and many others. Assist in redesign & renovation of existing products to increase quality, reduce costs, and/or increase production efficiencies. Partner internally and extenally to source new ingredients and leverage vendor expertise in ingredient functionality. Assist Quality and Procurement departments in maintaining specifications for new ingredients and/or suppliers. Supoport production with troubleshooting out of spec product or production concerns on established products. Maintain accurate product records, documentation and archives in various databases including global data synchronization of existing retail business. Maintain laboratory, including upkeep of equipment, stocking of supplies, and general cleaning of work areas. Performs other related and assigned duties as necessary. Minimun Qualifications Must hold a Bachelors degree in Food Science from an accredited University. Previous experience in food product development & food manufacturing strongly preferred. Ability and interest to work in laboratory, pilot plant and manufacturing scale environments. Proven ability to manage multiple assignments/tasks. Ability to work independently while collaborating and communicating with team members in various departments. Strong communication skills (oral and written). Knowlege of Genesis labeling system preferred but not required. Must be physically capable of lifting 50lbs. weight restriction. | 4 | 0.5% |
| What We Do: At the SEI Emerging Technology Center, we describe our work as “making the recently possible mission-practical.” We help our government customers stay at the leading edge of technology by identifying, demonstrating, extending, and applying emerging software technologies to solve real government problems. We currently work in the fields of human-machine interaction, applied artificial intelligence and machine learning, and advanced computing—areas that are changing and progressing rapidly. As we show our customers how new technologies can improve their mission capabilities through rapid prototyping and iterative development, we both rely on and shape academic and industrial research. Are you creative, curious, energetic, collaborative, technology-focused, and hard-working? Are you interested in making a difference by bringing innovation to government organizations and beyond? Apply to join our team. Position Summary: As a senior research scientist focusing on machine learning, you will identify, shape, apply, conduct, and lead research that matches critical U.S. government needs. Requirements: BS in Computer Science or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years of experience; OR PhD with five (5) years of experience. Flexible to travel to other SEI offices in Pittsburgh and Washington, DC, sponsor sites, conferences, and offsite meetings on occasion. Moderate (25") travel outside of your home location. You will be subject to a background investigation and must be eligible to obtain and maintain a Department of Defense security clearance. Duties: Hands-on research: You’ll conduct and lead novel research in applied machine learning and artificial intelligence. Solution development: You’ll work with and lead interdisciplinary teams to turn research results into prototype operational capabilities for government customers and stakeholders. Strategy: You’ll work with Center leaders and colleagues to plan, develop, and carry out an overall research strategy, and to influence the national research agenda regarding future technology. Collaboration: You'll actively participate on teams of software developers, researchers, designers, and technical leads. You'll build relationships and collaborate with researchers, government customers, and other stakeholders to understand challenges, needs, possible solutions, and research directions. Mentoring: You'll contribute to improving the overall technical capabilities of the Center by mentoring and teaching others, participating in design (software and otherwise) sessions, and sharing insights and wisdom across the SEI Emerging Technology Center team. Knowledge, Skills, and Abilities: Deep technical knowledge: You have performed extensive research in applied machine learning and artificial intelligence. You have worked with tools, techniques, algorithms, software, and programming languages for deep learning, reinforcement learning, statistics, sensors and sensor fusion, planning, computer vision, or related areas. Communication and Collaboration: You have strong written and verbal communication skills and can interact collaboratively and diplomatically with customers and colleagues. You grasp the big picture, direction, and goals of an effort while focusing great attention to detail. You can present complex ideas to people who may not have a deep understanding of the subject area. Dedication: You can meet deadlines while multi-tasking–sometimes under pressure and with shifting priorities. Creativity and Innovation: You are creative and curious, and you are inspired by the prospect of collaborating with premier researchers and visionaries at Carnegie Mellon and other universities and organizations. You quickly learn new procedures, techniques, and approaches. You are forward-looking and can connect research with practical challenges. Knowledge and Learning: You possess broad technical interests along with a deep knowledge of a particular field such as human-computer interaction, data analytics and machine learning, advanced computing, and autonomy and adaptive systems. Desired Experience: Research practices and publications: You have a track record of conducting research in machine learning and artificial intelligence. You have a reputation for the highest level of research and technical integrity. You have demonstrated contributions and have published research. Familiarity with emerging trends and opportunities: You are familiar with technical challenges and emerging trends in computing and information science, and you are aware of opportunities in industry and government. Technical leadership: You have led research projects and have experience collaborating across research teams and mentoring other researchers. Proposals: You have formulated and delivered successful research proposals to funding agencies and led the resulting projects. Government projects: You have worked or are familiar with DARPA, IARPA, Service Labs, or other government research sponsors More Information Please visit “Why Carnegie Mellon” to learn more about becoming part of an institution inspiring innovations that change the world. A listing of employee benefits is available at: www.cmu.edu/jobs/benefits-at-a-glance/. Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran. | 4 | 0.5% |
| We have an opportunity to join the Alliance as the Analytics Manager - Data Mart leading in the Analytics Services Department. WHAT YOU'LL BE RESPONSIBLE FOR Reporting to the Analytics Director, you will manage and lead the analytical data management function, including the gathering and assessment of business information needs for enterprise analytics and preparation of system requirements in order to create a single source of truth. Manage and lead the business information solution based Key Performance Indicator (KPI) dashboard reporting, customization, training and related integration with the Enterprise Data Warehouse (EDW). You will also manage, supervise, mentor and train assigned staff. ABOUT THE TEAM Our Analytics teams are skilled, focused, and highly collaborative. We work hard, have fun and take pride in how our work impacts the health outcomes for the communities we serve. THE IDEAL CANDIDATE WILL HAVE Passion and drive to roll up their sleeves and be a hands on manager Expertise and passion in the design, maintenance and evaluation of comprehensive analytical data marts Strength in developing data management plans, data dictionaries Experience and excitement for developing new teams and processes Strong communication skills and the ability to partner with cross-functional teams Ability to lead and inspire others, while promoting an environment that supports professional growth, embraces complex challenges and celebrates accomplishments WHAT YOU'LL NEED TO BE SUCCESSFUL To read the full position description, and list of requirements click here. Knowledge of: Data warehouse and analytical data mart concepts SQL Tools and techniques of data analysis and information reporting Thorough knowledge of information repository issues and concepts Joint Application Design (JAD) facilitation or other requirements-gathering techniques Relational database concepts and the creation of queries and reports using SQL and Tableau Data elements and their relationship to data quality requirements Ability to: Develop work plans and workflows and organize and prioritize analytical data mart activities Interpret and apply complex principles, policies, terms and procedures Define issues, interpret data, identify solutions, and make recommendations for action Independently document, summarize and resolve complex issues and projects Train, mentor, supervise, and evaluate the work of staff Education and Experience: Bachelor's degree in Computer Science, Information Science or a related field and seven years of experience performing data analysis and manipulation which included some experience leading or supervising staff; or an equivalent combination of education and experience may be qualifying OUR BENEFITS Medical, Dental and Vision Plans Ample Paid Time Off 11 Paid Holidays per year 401(a) Retirement Plan 457 Deferred Compensation Plan Robust Health and Wellness Program EV Charging Stations And many more ABOUT US We are a group of over 500 dedicated employees, committed to our mission of providing accessible, quality health care that is guided by local innovation. We feel that our work is bigger than ourselves. We leave work each day knowing that we made a difference in the community around us. Join us at Central California Alliance for Health (the Alliance), where you will be part of a culture that is respectful, diverse, professional and fun, and where you are empowered to do your best work. As a regional non-profit health plan, we serve over 330,000 members in Santa Cruz, Monterey and Merced counties. To learn more about us, click here or check out this video. At this time the Alliance does not provide any type of sponsorship. Applicants must be currently authorized to work in the United States on a full-time, ongoing basis without current or future needs for sponsorship. | 4 | 0.5% |
| As we strive to make a better day for our guests and team members, we look to enhance our enterprise applications dev team / master data efforts by adding someone with experience in Java. You will: 1. Develop solutions to support the initiative of moving our technology stack to the cloud 2. Maintain and develop solutions on SQL Server / PostgreSQL database leveraging tables, stored procedures, views, database roles, etc 3. Utilize a scripting language for automation of manual processes and manipulation/massage of data 4. Design solutions, document findings (gaps and risks), and communicate information and results to business partners in a concise and repeatable manner 5. Maintain up-to-date knowledge of industry standards for ETL tools and MDM technical solutions 6. Develop and maintain APIs using both MuleSoft and native EBX APIs Requirements: Java experience required. Experience with the Software Development Lifecycle (SDLC) required. Source control experience required. GITHUB, Subversion, or equivalent preferred Experience using query languages within relational database management systems (RDBMS). PostgreSQL and SQL Server are preferred. Python or shell scripting experience is a plus. .NET development experience is a plus. Release Management / Configuration Management / CICD experience a plus Experience with Maven, Jenkins, and SonarQube a plus Experience with large volumes of data using an established Enterprise Data Warehouse a plus Data extract, transform and load experience with an enterprise solution such as Informatica, SSIS, or Talend, is a plus. Experience using REST/SOAP APIs and MuleSoft experience a plus. Ability to troubleshoot and resolve issues independently is a plus. Attention to detail and strong problem solving skills desired. Ability to work as a member of a team to achieve stated goals. Job Type: Contract Experience: Java: 3 years (Required) SDLC: 2 years (Preferred) PostgreSQL and SQL: 2 years (Required) Location: Knoxville, TN (Required) Work authorization: United States (Required) Work Location: One location Benefits: Health insurance Schedule:: Monday to Friday | 4 | 0.5% |
| Day Shift: 7A-330P. Holidays and every other weekend. Summary: Performs, calculates and reports routine and special laboratory tests. Maintains equipment and troubleshoots problems. Evaluates results and quality control data. Serves as a resource and teaches new employees and students. Assists in evaluating new test procedures. The individual in this position must demonstrate knowledge of the principles of growth and development over the life span of the patient. In addition, she/he must possess the ability to assess patient data relative to age specific needs and provide care as described in the department's policies and procedures. Other information: Will consider entry level graduates of an approved associate degree program in an appropriate science field and eligibility as medical technologist.Must complete Point of Care Testing training as part of Department Orientation. Able to communicate effectively, pleasantly, cooperatively, and discretely with patients, physicians, hospital employees, and the general public. Able to work under pressure. Willingness to increase knowledge of laboratory/hospital functions. Demonstration of creativity, initiative and problem solving. Associate Degree in an appropriate science field and medical technologist certification or eligible. Responsibilities: Demonstrates technical knowledge and competence in performing expected responsibilities. Ability to perform laboratory skills. Performs and evaluates maintenance systems. Implements corrective action as appropriate. Performs and evaluates quality control data and implements corrective action. Ability to identify issues and processes requiring improvement. Ability to find, organize and use resources to improve outcomes. Monitors and evaluates training progress and makes recommendations for additional training. Assists in maintenance and ordering of supplies. Helps maintain organization and cleanliness of work/storage areas. Demonstrates the ability to function productively and independently, planning and prioritizing times and tasks to complete work assignments. Ability to maintain positive performance under a variety of conditions. Credentials: Essential: ASCP-MEDTECH - MLT OR MT Competencies and skills: Essential: Clear Communication Skills Both Written And Verbal Able To Keep Confidential Information Regarding Patients, Team Members Able To Withstand Crisis Situations Has Skills To Provides Customer Service To Patients, Team Members And Visitors Knowledge And Experience With Electronic Health Records Education: Essential: Associates Degree in related field Education specialization: Essential: Medical Technology Location: Millville All Services Shift : Flexible-hours/shifts may vary depending on department needs FTE: 0.500000 Work Status: Part Time >32 | 4 | 0.5% |
| ABL is seeking a Staff Scientist for the Downstream Process Development team. The candidate will be responsible for planning, development, and optimization, execution of assigned commercial and government client downstream development tasks. Working with external clients, R&D, Quality Control/ Quality Assurance and GMP Manufacturing, the scientist will provide expertise and scientific leadership for design, development, optimization, and production of protein therapeutics and viral vectors. The successful candidate will contribute to the team based execution of projects. The main responsibilities will include but not limited to follows: Develop robust, high-yield and scalable purification process (recombinant protein, virus and virus like particles) for Vaccine Development of Phase I/II candidates. Develop, optimize and scale-up protein purification methods to meet cGMP and Regulatory Compliance using Design of Experiment (DOE) methods. Lead efforts to evaluate different resins, filters, and analytical methods pertinent to purification development activities. Perform experiments using AKTA series Chromatography skids, TFF systems, and industry standard Harvest methods scale. Interacts with other departments involved in GMP manufacturing for planning production, testing and product release in a timely manner resulting in successful completion of projects. Participate in technology transfer of processes to manufacturing and from external clients, and from process development to manufacturing. Generate, manage, and maintain critical data in a highly organized manner in the form of notebook, protocol and SOP. Provide progress and developmental reports for assessment by clients. Develop and draft production batch records for GMP manufacturing, support and troubleshooting GMP production activities. Perform experiments and deliver results under minimal supervision, and within tight time lines, to a prescribed budget for internal / external client projects. Job Requirements This position requires a PhD in a life science discipline (Biochemistry, Analytical Chemistry Protein Chemistry or other related discipline), with 3-5 years of experience in Downstream Process Development, or an MS with 5-10 years’ experience, or a BS degree with more than 10 years of experience. Experience with cGMP manufacturing under cGMP/cGLP compliance a plus. Experience with AKTA purification systems. Computer skills using MS Office (Word, Excel, and Power Point). Proven leadership skills. Possess excellent interpersonal skills, both communications and written. Must be able to communicate effectively with all echelons of Management and staff. Task & Team-oriented, analytical, organized, detail-oriented, self-motivated & ability to multi-task. Travel Expectation None ABL, Inc. participates in E-Verify, an Internet-based system of the Department of Homeland Security (DHS) and Social Security Administration, that allows us to determine an employee's eligibility to work in the United States. ADDITIONAL INFORMATION: Candidate must meet all the requirements of our Company Occupational Health program as directed by the Occupational Health Consultant to include pre-employment physical and drug screen. Candidates are encouraged to submit a resume and a cover letter outlining background and experience as it relates to the position requirements and salary history/requirements. Please note that “negotiable” is neither salary nor requirements. Salary commensurate with experience. ABL, Inc. does not accept nor respond to unsolicited resumes from vendors, including recruitment agencies and search firms. Approved recruiting agencies must obtain prior approval from ABL, Inc. Human Resources in order to submit resumes to ABL, Inc. for consideration. | 4 | 0.5% |
| Who We Are! At Maven Wave, we are relentless in hiring the industry’s top talent. Each employee is hand-picked not only for their skills, but for their personality and broad expertise. We are looking for this rare combination of talent that sets us apart in the industry. Maven Wave helps leading companies make the shift to digital and shorten the fuse to innovation. We combine the expertise of top-tier consulting with the agility of a cutting-edge technology firm. This multidisciplinary blend of skills allows us to create unique digital advantages for our clients. Maven Wave’s digital solutions are agile, mobile, rooted in analytics, and built in the cloud. Maven Wave, Google, and YOU: Drive and deliver business results with data-based insights. We are looking for a Senior Data Scientist who will utilize their analytical, statistical, and programming skills to develop data-driven solutions to complex business challenges. Your Life As a Maven: Leverage company data to drive business solutions for enterprise clients using R and Python. Perform data collection for Data Science operations including Machine Learning. Develop custom data models and algorithms to apply to data sets. Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes. Assess Model accuracy using common metrics (AUC, F1, etc.) and explain the results to client stakeholders. Your Expertise: 7+ years of experience manipulating data sets and building statistical models. Cloud experience in a major platform, such as AWS, GCP, or Azure. Experience using Data Science languages (R, Python) to manipulate data and draw insights. Knowledge of a variety of Machine Learning and advanced analytical techniques and their real world advantages/drawbacks. Familiarity with the following software/tools: Python, C, Java, Jupyter Notebooks, SQL, ML platforms (H2O, DataRobot), distributed data (Map/Reduce, Hadoop), and visualization (Tableau, qikview) Your X-Factor: Aptitude - You have an innate capacity to transition from project to project without skipping a beat. Communication - You have excellent written and verbal communication skills for coordination across projects and teams. Impact - You are a critical thinker with an emphasis on creativity and innovation. Passion - You have the drive to succeed paired with a continuous hunger to learn. Leadership - You are trusted, empathetic, accountable, and empower others around you. Why We’re Proud To Be Mavens! Google Cloud North America Services Partner of the Year 2019, 2018 #21 Best Workplaces in Chicago, FORTUNE, 2018 Great Place To Work Certification, Great Place to Work, 2017 & 2018 Fast Fifty, Crain's Chicago Business 101 Best and Brightest Companies to Work For, National Association for Business Resources (NABR) Top Google Cloud Partner, Clutch Fastest Growing Consulting Firms in North America (#11, #37), Consulting Magazine Top IT Services Companies, Clutch Google Global Rising Star Partner of the Year Ready to Learn More? Life as a Maven Check out the Apps and Data Team See what Glassdoor has to say Real Customer Stories | 3 | 0.4% |
| Other values (453) | 703 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| and | 23732 | 5.9% |
| to | 12880 | 3.2% |
| the | 9875 | 2.5% |
| of | 9471 | 2.4% |
| data | 7644 | 1.9% |
| in | 7083 | 1.8% |
| a | 6493 | 1.6% |
| with | 5980 | 1.5% |
| for | 4373 | 1.1% |
| experience | 3782 | 0.9% |
| Other values (13072) | 311377 |
Most occurring characters
| Value | Count | Frequency (%) |
| 378632 | ||
| e | 269480 | 9.4% |
| i | 198961 | 6.9% |
| a | 197301 | 6.9% |
| t | 194588 | 6.8% |
| n | 188423 | 6.6% |
| o | 168053 | 5.9% |
| s | 148718 | 5.2% |
| r | 148615 | 5.2% |
| l | 106053 | 3.7% |
| Other values (108) | 872379 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 2280886 | |
| Space Separator | 378647 | 13.2% |
| Uppercase Letter | 97486 | 3.4% |
| Other Punctuation | 59551 | 2.1% |
| Control | 30145 | 1.0% |
| Dash Punctuation | 9122 | 0.3% |
| Decimal Number | 7453 | 0.3% |
| Close Punctuation | 2611 | 0.1% |
| Open Punctuation | 2579 | 0.1% |
| Final Punctuation | 1294 | < 0.1% |
| Other values (9) | 1429 | < 0.1% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| e | 269480 | |
| i | 198961 | 8.7% |
| a | 197301 | 8.7% |
| t | 194588 | 8.5% |
| n | 188423 | 8.3% |
| o | 168053 | 7.4% |
| s | 148718 | 6.5% |
| r | 148615 | 6.5% |
| l | 106053 | 4.6% |
| c | 91769 | 4.0% |
| Other values (20) | 568925 |
Uppercase Letter
| Value | Count | Frequency (%) |
| S | 10349 | 10.6% |
| A | 9041 | 9.3% |
| E | 7349 | 7.5% |
| D | 6544 | 6.7% |
| P | 6399 | 6.6% |
| T | 6206 | 6.4% |
| C | 6122 | 6.3% |
| I | 5365 | 5.5% |
| M | 5227 | 5.4% |
| R | 4459 | 4.6% |
| Other values (17) | 30425 |
Other Punctuation
| Value | Count | Frequency (%) |
| , | 29385 | |
| . | 17714 | |
| / | 3506 | 5.9% |
| : | 3251 | 5.5% |
| ' | 1172 | 2.0% |
| ; | 886 | 1.5% |
| • | 855 | 1.4% |
| & | 799 | 1.3% |
| * | 414 | 0.7% |
| ? | 346 | 0.6% |
| Other values (9) | 1223 | 2.1% |
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 1947 | |
| 2 | 1249 | |
| 1 | 1231 | |
| 5 | 725 | 9.7% |
| 3 | 724 | 9.7% |
| 4 | 511 | 6.9% |
| 7 | 313 | 4.2% |
| 8 | 268 | 3.6% |
| 6 | 263 | 3.5% |
| 9 | 222 | 3.0% |
Math Symbol
| Value | Count | Frequency (%) |
| + | 666 | |
| > | 34 | 4.5% |
| = | 31 | 4.1% |
| | | 21 | 2.8% |
| ~ | 8 | 1.1% |
Other Symbol
| Value | Count | Frequency (%) |
| ● | 85 | |
| ® | 39 | |
| ™ | 19 | 13.1% |
| © | 2 | 1.4% |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 8900 | |
| – | 163 | 1.8% |
| — | 59 | 0.6% |
Close Punctuation
| Value | Count | Frequency (%) |
| ) | 2532 | |
| ] | 74 | 2.8% |
| } | 5 | 0.2% |
Open Punctuation
| Value | Count | Frequency (%) |
| ( | 2529 | |
| [ | 42 | 1.6% |
| { | 8 | 0.3% |
Space Separator
| Value | Count | Frequency (%) |
| 378632 | ||
| 15 | < 0.1% |
Final Punctuation
| Value | Count | Frequency (%) |
| ’ | 1187 | |
| ” | 107 | 8.3% |
Initial Punctuation
| Value | Count | Frequency (%) |
| “ | 105 | |
| ‘ | 9 | 7.9% |
Other Number
| Value | Count | Frequency (%) |
| ⅓ | 9 | |
| ³ | 2 | 18.2% |
Control
| Value | Count | Frequency (%) |
| 30145 |
Connector Punctuation
| Value | Count | Frequency (%) |
| _ | 322 |
Currency Symbol
| Value | Count | Frequency (%) |
| $ | 66 |
Format
| Value | Count | Frequency (%) |
| | 8 |
Private Use
| Value | Count | Frequency (%) |
| | 2 |
Other Letter
| Value | Count | Frequency (%) |
| º | 1 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 2378373 | |
| Common | 492828 | 17.2% |
| Unknown | 2 | < 0.1% |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 378632 | ||
| 30145 | 6.1% | |
| , | 29385 | 6.0% |
| . | 17714 | 3.6% |
| - | 8900 | 1.8% |
| / | 3506 | 0.7% |
| : | 3251 | 0.7% |
| ) | 2532 | 0.5% |
| ( | 2529 | 0.5% |
| 0 | 1947 | 0.4% |
| Other values (49) | 14287 | 2.9% |
Latin
| Value | Count | Frequency (%) |
| e | 269480 | |
| i | 198961 | 8.4% |
| a | 197301 | 8.3% |
| t | 194588 | 8.2% |
| n | 188423 | 7.9% |
| o | 168053 | 7.1% |
| s | 148718 | 6.3% |
| r | 148615 | 6.2% |
| l | 106053 | 4.5% |
| c | 91769 | 3.9% |
| Other values (48) | 666412 |
Unknown
| Value | Count | Frequency (%) |
| | 2 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 2868136 | |
| Punctuation | 2523 | 0.1% |
| None | 425 | < 0.1% |
| Geometric Shapes | 85 | < 0.1% |
| Letterlike Symbols | 19 | < 0.1% |
| Number Forms | 9 | < 0.1% |
| Alphabetic PF | 4 | < 0.1% |
| PUA | 2 | < 0.1% |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 378632 | ||
| e | 269480 | 9.4% |
| i | 198961 | 6.9% |
| a | 197301 | 6.9% |
| t | 194588 | 6.8% |
| n | 188423 | 6.6% |
| o | 168053 | 5.9% |
| s | 148718 | 5.2% |
| r | 148615 | 5.2% |
| l | 106053 | 3.7% |
| Other values (83) | 869312 |
Punctuation
| Value | Count | Frequency (%) |
| ’ | 1187 | |
| • | 855 | |
| – | 163 | 6.5% |
| ” | 107 | 4.2% |
| “ | 105 | 4.2% |
| — | 59 | 2.3% |
| … | 23 | 0.9% |
| 15 | 0.6% | |
| ‘ | 9 | 0.4% |
None
| Value | Count | Frequency (%) |
| · | 337 | |
| ® | 39 | 9.2% |
| Â | 18 | 4.2% |
| | 8 | 1.9% |
| ï | 8 | 1.9% |
| é | 5 | 1.2% |
| § | 3 | 0.7% |
| © | 2 | 0.5% |
| ³ | 2 | 0.5% |
| è | 2 | 0.5% |
Geometric Shapes
| Value | Count | Frequency (%) |
| ● | 85 |
Letterlike Symbols
| Value | Count | Frequency (%) |
| ™ | 19 |
Number Forms
| Value | Count | Frequency (%) |
| ⅓ | 9 |
Alphabetic PF
| Value | Count | Frequency (%) |
| fi | 4 |
PUA
| Value | Count | Frequency (%) |
| | 2 |
| Distinct | 31 |
|---|---|
| Distinct (%) | 4.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 3.618867925 |
| Minimum | -1 |
|---|---|
| Maximum | 5 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 11 |
| Negative (%) | 1.5% |
| Memory size | 5.9 KiB |
Quantile statistics
| Minimum | -1 |
|---|---|
| 5-th percentile | 2.6 |
| Q1 | 3.3 |
| median | 3.7 |
| Q3 | 4 |
| 95-th percentile | 4.7 |
| Maximum | 5 |
| Range | 6 |
| Interquartile range (IQR) | 0.7 |
Descriptive statistics
| Standard deviation | 0.8012101585 |
|---|---|
| Coefficient of variation (CV) | 0.2213980104 |
| Kurtosis | 14.30412724 |
| Mean | 3.618867925 |
| Median Absolute Deviation (MAD) | 0.35 |
| Skewness | -2.814019554 |
| Sum | 2685.2 |
| Variance | 0.641937718 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=31)
| Value | Count | Frequency (%) |
| 3.9 | 63 | 8.5% |
| 3.8 | 61 | 8.2% |
| 3.7 | 61 | 8.2% |
| 3.5 | 49 | 6.6% |
| 4 | 47 | 6.3% |
| 3.6 | 46 | 6.2% |
| 3.4 | 44 | 5.9% |
| 3.3 | 39 | 5.3% |
| 3.2 | 35 | 4.7% |
| 4.4 | 33 | 4.4% |
| Other values (21) | 264 |
| Value | Count | Frequency (%) |
| -1 | 11 | |
| 1.9 | 3 | 0.4% |
| 2.1 | 5 | 0.7% |
| 2.2 | 2 | 0.3% |
| 2.3 | 2 | 0.3% |
| 2.4 | 7 | |
| 2.5 | 2 | 0.3% |
| 2.6 | 12 | |
| 2.7 | 14 | |
| 2.8 | 7 |
| Value | Count | Frequency (%) |
| 5 | 5 | 0.7% |
| 4.8 | 9 | 1.2% |
| 4.7 | 31 | |
| 4.6 | 10 | 1.3% |
| 4.5 | 7 | 0.9% |
| 4.4 | 33 | |
| 4.3 | 32 | |
| 4.2 | 26 | |
| 4.1 | 19 | |
| 4 | 47 |
| Distinct | 343 |
|---|---|
| Distinct (%) | 46.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| MassMutual 3.6 | 14 |
|---|---|
| Reynolds American 3.1 | 14 |
| Takeda Pharmaceuticals 3.7 | 14 |
| Software Engineering Institute 2.6 | 11 |
| PNNL 3.8 | 10 |
| Other values (338) |
Length
| Max length | 55 |
|---|---|
| Median length | 37 |
| Mean length | 19.18059299 |
| Min length | 4 |
Characters and Unicode
| Total characters | 14232 |
|---|---|
| Distinct characters | 72 |
| Distinct categories | 8 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 161 ? |
|---|---|
| Unique (%) | 21.7% |
Sample
| 1st row | Tecolote Research 3.8 |
|---|---|
| 2nd row | University of Maryland Medical System 3.4 |
| 3rd row | KnowBe4 4.8 |
| 4th row | PNNL 3.8 |
| 5th row | Affinity Solutions 2.9 |
Common Values
| Value | Count | Frequency (%) |
| MassMutual 3.6 | 14 | 1.9% |
| Reynolds American 3.1 | 14 | 1.9% |
| Takeda Pharmaceuticals 3.7 | 14 | 1.9% |
| Software Engineering Institute 2.6 | 11 | 1.5% |
| PNNL 3.8 | 10 | 1.3% |
| Liberty Mutual Insurance 3.3 | 10 | 1.3% |
| AstraZeneca 3.9 | 9 | 1.2% |
| MITRE 3.2 | 8 | 1.1% |
| Numeric, LLC 3.2 | 7 | 0.9% |
| Advanced BioScience Laboratories 2.7 | 7 | 0.9% |
| Other values (333) | 638 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| 3.9 | 63 | 2.8% |
| 3.8 | 61 | 2.7% |
| 3.7 | 61 | 2.7% |
| 3.5 | 49 | 2.2% |
| 4.0 | 47 | 2.1% |
| 3.6 | 46 | 2.1% |
| 3.4 | 44 | 2.0% |
| 3.3 | 39 | 1.7% |
| health | 36 | 1.6% |
| 3.2 | 35 | 1.6% |
| Other values (537) | 1759 |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 1019 | 7.2% |
| a | 891 | 6.3% |
| . | 768 | 5.4% |
| 767 | 5.4% | |
| 731 | 5.1% | |
| n | 688 | 4.8% |
| t | 685 | 4.8% |
| i | 677 | 4.8% |
| r | 657 | 4.6% |
| o | 631 | 4.4% |
| Other values (62) | 6718 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 8433 | |
| Uppercase Letter | 1951 | 13.7% |
| Decimal Number | 1516 | 10.7% |
| Other Punctuation | 812 | 5.7% |
| Space Separator | 767 | 5.4% |
| Control | 731 | 5.1% |
| Dash Punctuation | 18 | 0.1% |
| Math Symbol | 4 | < 0.1% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| e | 1019 | |
| a | 891 | |
| n | 688 | 8.2% |
| t | 685 | 8.1% |
| i | 677 | 8.0% |
| r | 657 | 7.8% |
| o | 631 | 7.5% |
| s | 596 | 7.1% |
| l | 408 | 4.8% |
| c | 408 | 4.8% |
| Other values (16) | 1773 |
Uppercase Letter
| Value | Count | Frequency (%) |
| C | 192 | 9.8% |
| S | 178 | 9.1% |
| T | 151 | 7.7% |
| A | 148 | 7.6% |
| I | 135 | 6.9% |
| L | 122 | 6.3% |
| M | 117 | 6.0% |
| P | 111 | 5.7% |
| R | 109 | 5.6% |
| E | 94 | 4.8% |
| Other values (16) | 594 |
Decimal Number
| Value | Count | Frequency (%) |
| 3 | 520 | |
| 4 | 304 | |
| 2 | 152 | 10.0% |
| 7 | 107 | 7.1% |
| 9 | 86 | 5.7% |
| 0 | 79 | 5.2% |
| 8 | 78 | 5.1% |
| 6 | 70 | 4.6% |
| 5 | 63 | 4.2% |
| 1 | 57 | 3.8% |
Other Punctuation
| Value | Count | Frequency (%) |
| . | 768 | |
| , | 21 | 2.6% |
| & | 13 | 1.6% |
| ' | 8 | 1.0% |
| / | 2 | 0.2% |
Math Symbol
| Value | Count | Frequency (%) |
| < | 2 | |
| > | 2 |
Space Separator
| Value | Count | Frequency (%) |
| 767 |
Control
| Value | Count | Frequency (%) |
| 731 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 18 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 10384 | |
| Common | 3848 | 27.0% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| e | 1019 | 9.8% |
| a | 891 | 8.6% |
| n | 688 | 6.6% |
| t | 685 | 6.6% |
| i | 677 | 6.5% |
| r | 657 | 6.3% |
| o | 631 | 6.1% |
| s | 596 | 5.7% |
| l | 408 | 3.9% |
| c | 408 | 3.9% |
| Other values (42) | 3724 |
Common
| Value | Count | Frequency (%) |
| . | 768 | |
| 767 | ||
| 731 | ||
| 3 | 520 | |
| 4 | 304 | 7.9% |
| 2 | 152 | 4.0% |
| 7 | 107 | 2.8% |
| 9 | 86 | 2.2% |
| 0 | 79 | 2.1% |
| 8 | 78 | 2.0% |
| Other values (10) | 256 | 6.7% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 14232 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| e | 1019 | 7.2% |
| a | 891 | 6.3% |
| . | 768 | 5.4% |
| 767 | 5.4% | |
| 731 | 5.1% | |
| n | 688 | 4.8% |
| t | 685 | 4.8% |
| i | 677 | 4.8% |
| r | 657 | 4.6% |
| o | 631 | 4.4% |
| Other values (62) | 6718 |
| Distinct | 200 |
|---|---|
| Distinct (%) | 27.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| New York, NY | |
|---|---|
| San Francisco, CA | 49 |
| Cambridge, MA | 47 |
| Chicago, IL | 32 |
| Boston, MA | 23 |
| Other values (195) |
Length
| Max length | 33 |
|---|---|
| Median length | 22 |
| Mean length | 13.1509434 |
| Min length | 8 |
Characters and Unicode
| Total characters | 9758 |
|---|---|
| Distinct characters | 54 |
| Distinct categories | 5 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 71 ? |
|---|---|
| Unique (%) | 9.6% |
Sample
| 1st row | Albuquerque, NM |
|---|---|
| 2nd row | Linthicum, MD |
| 3rd row | Clearwater, FL |
| 4th row | Richland, WA |
| 5th row | New York, NY |
Common Values
| Value | Count | Frequency (%) |
| New York, NY | 55 | 7.4% |
| San Francisco, CA | 49 | 6.6% |
| Cambridge, MA | 47 | 6.3% |
| Chicago, IL | 32 | 4.3% |
| Boston, MA | 23 | 3.1% |
| San Jose, CA | 13 | 1.8% |
| Pittsburgh, PA | 12 | 1.6% |
| Washington, DC | 11 | 1.5% |
| Rockville, MD | 11 | 1.5% |
| Winston-Salem, NC | 10 | 1.3% |
| Other values (190) | 479 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| ca | 152 | 8.8% |
| ma | 103 | 5.9% |
| san | 86 | 5.0% |
| ny | 72 | 4.2% |
| new | 57 | 3.3% |
| francisco | 57 | 3.3% |
| york | 55 | 3.2% |
| cambridge | 48 | 2.8% |
| va | 41 | 2.4% |
| il | 40 | 2.3% |
| Other values (256) | 1023 |
Most occurring characters
| Value | Count | Frequency (%) |
| 992 | 10.2% | |
| , | 743 | 7.6% |
| a | 607 | 6.2% |
| o | 533 | 5.5% |
| n | 529 | 5.4% |
| e | 514 | 5.3% |
| i | 493 | 5.1% |
| A | 438 | 4.5% |
| r | 424 | 4.3% |
| l | 348 | 3.6% |
| Other values (44) | 4137 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 5525 | |
| Uppercase Letter | 2488 | |
| Space Separator | 992 | 10.2% |
| Other Punctuation | 743 | 7.6% |
| Dash Punctuation | 10 | 0.1% |
Most frequent character per category
Uppercase Letter
| Value | Count | Frequency (%) |
| A | 438 | |
| C | 336 | |
| N | 211 | 8.5% |
| M | 202 | 8.1% |
| S | 153 | 6.1% |
| Y | 133 | 5.3% |
| L | 106 | 4.3% |
| F | 93 | 3.7% |
| I | 89 | 3.6% |
| D | 87 | 3.5% |
| Other values (16) | 640 |
Lowercase Letter
| Value | Count | Frequency (%) |
| a | 607 | |
| o | 533 | |
| n | 529 | |
| e | 514 | |
| i | 493 | 8.9% |
| r | 424 | 7.7% |
| l | 348 | 6.3% |
| t | 320 | 5.8% |
| s | 276 | 5.0% |
| c | 228 | 4.1% |
| Other values (15) | 1253 |
Space Separator
| Value | Count | Frequency (%) |
| 992 |
Other Punctuation
| Value | Count | Frequency (%) |
| , | 743 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 10 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 8013 | |
| Common | 1745 | 17.9% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| a | 607 | 7.6% |
| o | 533 | 6.7% |
| n | 529 | 6.6% |
| e | 514 | 6.4% |
| i | 493 | 6.2% |
| A | 438 | 5.5% |
| r | 424 | 5.3% |
| l | 348 | 4.3% |
| C | 336 | 4.2% |
| t | 320 | 4.0% |
| Other values (41) | 3471 |
Common
| Value | Count | Frequency (%) |
| 992 | ||
| , | 743 | |
| - | 10 | 0.6% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 9758 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 992 | 10.2% | |
| , | 743 | 7.6% |
| a | 607 | 6.2% |
| o | 533 | 5.5% |
| n | 529 | 5.4% |
| e | 514 | 5.3% |
| i | 493 | 5.1% |
| A | 438 | 4.5% |
| r | 424 | 4.3% |
| l | 348 | 3.6% |
| Other values (44) | 4137 |
| Distinct | 198 |
|---|---|
| Distinct (%) | 26.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| New York, NY | 52 |
|---|---|
| San Francisco, CA | 42 |
| Chicago, IL | 30 |
| Cambridge, MA | 20 |
| Springfield, MA | 14 |
| Other values (193) |
Length
| Max length | 26 |
|---|---|
| Median length | 22 |
| Mean length | 13.606469 |
| Min length | 2 |
Characters and Unicode
| Total characters | 10096 |
|---|---|
| Distinct characters | 55 |
| Distinct categories | 6 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 70 ? |
|---|---|
| Unique (%) | 9.4% |
Sample
| 1st row | Goleta, CA |
|---|---|
| 2nd row | Baltimore, MD |
| 3rd row | Clearwater, FL |
| 4th row | Richland, WA |
| 5th row | New York, NY |
Common Values
| Value | Count | Frequency (%) |
| New York, NY | 52 | 7.0% |
| San Francisco, CA | 42 | 5.7% |
| Chicago, IL | 30 | 4.0% |
| Cambridge, MA | 20 | 2.7% |
| Springfield, MA | 14 | 1.9% |
| Boston, MA | 14 | 1.9% |
| Winston-Salem, NC | 14 | 1.9% |
| OSAKA, Japan | 14 | 1.9% |
| Richland, WA | 12 | 1.6% |
| Reston, VA | 12 | 1.6% |
| Other values (188) | 518 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| ca | 169 | 9.5% |
| ma | 86 | 4.8% |
| san | 76 | 4.3% |
| ny | 63 | 3.5% |
| new | 55 | 3.1% |
| va | 53 | 3.0% |
| york | 52 | 2.9% |
| francisco | 46 | 2.6% |
| il | 34 | 1.9% |
| chicago | 30 | 1.7% |
| Other values (261) | 1111 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1033 | 10.2% | |
| , | 741 | 7.3% |
| a | 647 | 6.4% |
| n | 615 | 6.1% |
| e | 560 | 5.5% |
| o | 528 | 5.2% |
| i | 510 | 5.1% |
| A | 459 | 4.5% |
| r | 422 | 4.2% |
| l | 370 | 3.7% |
| Other values (45) | 4211 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 5779 | |
| Uppercase Letter | 2525 | |
| Space Separator | 1033 | 10.2% |
| Other Punctuation | 741 | 7.3% |
| Dash Punctuation | 17 | 0.2% |
| Decimal Number | 1 | < 0.1% |
Most frequent character per category
Uppercase Letter
| Value | Count | Frequency (%) |
| A | 459 | |
| C | 350 | |
| N | 192 | 7.6% |
| S | 181 | 7.2% |
| M | 178 | 7.0% |
| Y | 115 | 4.6% |
| L | 107 | 4.2% |
| F | 102 | 4.0% |
| I | 82 | 3.2% |
| P | 80 | 3.2% |
| Other values (16) | 679 |
Lowercase Letter
| Value | Count | Frequency (%) |
| a | 647 | |
| n | 615 | |
| e | 560 | |
| o | 528 | |
| i | 510 | 8.8% |
| r | 422 | 7.3% |
| l | 370 | 6.4% |
| t | 348 | 6.0% |
| s | 259 | 4.5% |
| d | 237 | 4.1% |
| Other values (15) | 1283 |
Space Separator
| Value | Count | Frequency (%) |
| 1033 |
Other Punctuation
| Value | Count | Frequency (%) |
| , | 741 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 17 |
Decimal Number
| Value | Count | Frequency (%) |
| 1 | 1 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 8304 | |
| Common | 1792 | 17.7% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| a | 647 | 7.8% |
| n | 615 | 7.4% |
| e | 560 | 6.7% |
| o | 528 | 6.4% |
| i | 510 | 6.1% |
| A | 459 | 5.5% |
| r | 422 | 5.1% |
| l | 370 | 4.5% |
| C | 350 | 4.2% |
| t | 348 | 4.2% |
| Other values (41) | 3495 |
Common
| Value | Count | Frequency (%) |
| 1033 | ||
| , | 741 | |
| - | 17 | 0.9% |
| 1 | 1 | 0.1% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 10096 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 1033 | 10.2% | |
| , | 741 | 7.3% |
| a | 647 | 6.4% |
| n | 615 | 6.1% |
| e | 560 | 5.5% |
| o | 528 | 5.2% |
| i | 510 | 5.1% |
| A | 459 | 4.5% |
| r | 422 | 4.2% |
| l | 370 | 3.7% |
| Other values (45) | 4211 |
| Distinct | 8 |
|---|---|
| Distinct (%) | 1.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 1001 - 5000 | |
|---|---|
| 501 - 1000 | |
| 10000+ | |
| 201 - 500 | |
| 51 - 200 | |
| Other values (3) |
Length
| Max length | 13 |
|---|---|
| Median length | 11 |
| Mean length | 10.07412399 |
| Min length | 7 |
Characters and Unicode
| Total characters | 7475 |
|---|---|
| Distinct characters | 12 |
| Distinct categories | 5 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 501 - 1000 |
|---|---|
| 2nd row | 10000+ |
| 3rd row | 501 - 1000 |
| 4th row | 1001 - 5000 |
| 5th row | 51 - 200 |
Common Values
| Value | Count | Frequency (%) |
| 1001 - 5000 | 150 | |
| 501 - 1000 | 134 | |
| 10000+ | 130 | |
| 201 - 500 | 117 | |
| 51 - 200 | 94 | |
| 5001 - 10000 | 76 | |
| 1 - 50 | 31 | 4.2% |
| unknown | 10 | 1.3% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 602 | ||
| 10000 | 206 | 10.6% |
| 1001 | 150 | 7.7% |
| 5000 | 150 | 7.7% |
| 501 | 134 | 6.9% |
| 1000 | 134 | 6.9% |
| 201 | 117 | 6.0% |
| 500 | 117 | 6.0% |
| 51 | 94 | 4.8% |
| 200 | 94 | 4.8% |
| Other values (4) | 148 | 7.6% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 2832 | |
| 1936 | ||
| 1 | 1092 | 14.6% |
| - | 602 | 8.1% |
| 5 | 602 | 8.1% |
| 2 | 211 | 2.8% |
| + | 130 | 1.7% |
| n | 30 | 0.4% |
| u | 10 | 0.1% |
| k | 10 | 0.1% |
| Other values (2) | 20 | 0.3% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 4737 | |
| Space Separator | 1936 | |
| Dash Punctuation | 602 | 8.1% |
| Math Symbol | 130 | 1.7% |
| Lowercase Letter | 70 | 0.9% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| n | 30 | |
| u | 10 | 14.3% |
| k | 10 | 14.3% |
| o | 10 | 14.3% |
| w | 10 | 14.3% |
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 2832 | |
| 1 | 1092 | 23.1% |
| 5 | 602 | 12.7% |
| 2 | 211 | 4.5% |
Space Separator
| Value | Count | Frequency (%) |
| 1936 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 602 |
Math Symbol
| Value | Count | Frequency (%) |
| + | 130 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 7405 | |
| Latin | 70 | 0.9% |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 2832 | |
| 1936 | ||
| 1 | 1092 | 14.7% |
| - | 602 | 8.1% |
| 5 | 602 | 8.1% |
| 2 | 211 | 2.8% |
| + | 130 | 1.8% |
Latin
| Value | Count | Frequency (%) |
| n | 30 | |
| u | 10 | 14.3% |
| k | 10 | 14.3% |
| o | 10 | 14.3% |
| w | 10 | 14.3% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 7475 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 2832 | |
| 1936 | ||
| 1 | 1092 | 14.6% |
| - | 602 | 8.1% |
| 5 | 602 | 8.1% |
| 2 | 211 | 2.8% |
| + | 130 | 1.7% |
| n | 30 | 0.4% |
| u | 10 | 0.1% |
| k | 10 | 0.1% |
| Other values (2) | 20 | 0.3% |
| Distinct | 102 |
|---|---|
| Distinct (%) | 13.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 1837.154987 |
| Minimum | -1 |
|---|---|
| Maximum | 2019 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 50 |
| Negative (%) | 6.7% |
| Memory size | 5.9 KiB |
Quantile statistics
| Minimum | -1 |
|---|---|
| 5-th percentile | -1 |
| Q1 | 1939 |
| median | 1988 |
| Q3 | 2007 |
| 95-th percentile | 2014 |
| Maximum | 2019 |
| Range | 2020 |
| Interquartile range (IQR) | 68 |
Descriptive statistics
| Standard deviation | 497.1837627 |
|---|---|
| Coefficient of variation (CV) | 0.270627011 |
| Kurtosis | 9.705374859 |
| Mean | 1837.154987 |
| Median Absolute Deviation (MAD) | 22 |
| Skewness | -3.394532023 |
| Sum | 1363169 |
| Variance | 247191.6939 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| -1 | 50 | 6.7% |
| 2010 | 32 | 4.3% |
| 2008 | 31 | 4.2% |
| 1996 | 27 | 3.6% |
| 2006 | 24 | 3.2% |
| 2012 | 21 | 2.8% |
| 2011 | 19 | 2.6% |
| 1958 | 18 | 2.4% |
| 2007 | 18 | 2.4% |
| 1984 | 18 | 2.4% |
| Other values (92) | 484 |
| Value | Count | Frequency (%) |
| -1 | 50 | |
| 1744 | 1 | 0.1% |
| 1781 | 14 | 1.9% |
| 1812 | 1 | 0.1% |
| 1830 | 4 | 0.5% |
| 1846 | 2 | 0.3% |
| 1849 | 7 | 0.9% |
| 1850 | 1 | 0.1% |
| 1851 | 14 | 1.9% |
| 1852 | 5 | 0.7% |
| Value | Count | Frequency (%) |
| 2019 | 2 | 0.3% |
| 2017 | 12 | 1.6% |
| 2016 | 5 | 0.7% |
| 2015 | 16 | |
| 2014 | 13 | |
| 2013 | 15 | |
| 2012 | 21 | |
| 2011 | 19 | |
| 2010 | 32 | |
| 2009 | 6 | 0.8% |
| Distinct | 9 |
|---|---|
| Distinct (%) | 1.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| Company - Private | |
|---|---|
| Company - Public | |
| Nonprofit Organization | |
| Subsidiary or Business Segment | 34 |
| Government | 15 |
| Other values (4) | 35 |
Length
| Max length | 30 |
|---|---|
| Median length | 17 |
| Mean length | 17.46091644 |
| Min length | 8 |
Characters and Unicode
| Total characters | 12956 |
|---|---|
| Distinct characters | 34 |
| Distinct categories | 5 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Company - Private |
|---|---|
| 2nd row | Other Organization |
| 3rd row | Company - Private |
| 4th row | Government |
| 5th row | Company - Private |
Common Values
| Value | Count | Frequency (%) |
| Company - Private | 410 | |
| Company - Public | 193 | |
| Nonprofit Organization | 55 | 7.4% |
| Subsidiary or Business Segment | 34 | 4.6% |
| Government | 15 | 2.0% |
| Hospital | 15 | 2.0% |
| College / University | 13 | 1.8% |
| Other Organization | 5 | 0.7% |
| School / School District | 2 | 0.3% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 618 | ||
| company | 603 | |
| private | 410 | |
| public | 193 | 9.0% |
| organization | 60 | 2.8% |
| nonprofit | 55 | 2.6% |
| segment | 34 | 1.6% |
| business | 34 | 1.6% |
| or | 34 | 1.6% |
| subsidiary | 34 | 1.6% |
| Other values (7) | 67 | 3.1% |
Most occurring characters
| Value | Count | Frequency (%) |
| 1400 | 10.8% | |
| a | 1182 | 9.1% |
| i | 925 | 7.1% |
| n | 889 | 6.9% |
| o | 858 | 6.6% |
| p | 673 | 5.2% |
| m | 652 | 5.0% |
| y | 650 | 5.0% |
| r | 628 | 4.8% |
| C | 616 | 4.8% |
| Other values (24) | 4483 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 9448 | |
| Uppercase Letter | 1490 | 11.5% |
| Space Separator | 1400 | 10.8% |
| Dash Punctuation | 603 | 4.7% |
| Other Punctuation | 15 | 0.1% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| a | 1182 | |
| i | 925 | |
| n | 889 | |
| o | 858 | |
| p | 673 | 7.1% |
| m | 652 | 6.9% |
| y | 650 | 6.9% |
| r | 628 | 6.6% |
| t | 611 | 6.5% |
| e | 586 | 6.2% |
| Other values (11) | 1794 |
Uppercase Letter
| Value | Count | Frequency (%) |
| C | 616 | |
| P | 603 | |
| S | 72 | 4.8% |
| O | 65 | 4.4% |
| N | 55 | 3.7% |
| B | 34 | 2.3% |
| G | 15 | 1.0% |
| H | 15 | 1.0% |
| U | 13 | 0.9% |
| D | 2 | 0.1% |
Space Separator
| Value | Count | Frequency (%) |
| 1400 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 603 |
Other Punctuation
| Value | Count | Frequency (%) |
| / | 15 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 10938 | |
| Common | 2018 | 15.6% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| a | 1182 | 10.8% |
| i | 925 | 8.5% |
| n | 889 | 8.1% |
| o | 858 | 7.8% |
| p | 673 | 6.2% |
| m | 652 | 6.0% |
| y | 650 | 5.9% |
| r | 628 | 5.7% |
| C | 616 | 5.6% |
| t | 611 | 5.6% |
| Other values (21) | 3254 |
Common
| Value | Count | Frequency (%) |
| 1400 | ||
| - | 603 | |
| / | 15 | 0.7% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 12956 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 1400 | 10.8% | |
| a | 1182 | 9.1% |
| i | 925 | 7.1% |
| n | 889 | 6.9% |
| o | 858 | 6.6% |
| p | 673 | 5.2% |
| m | 652 | 5.0% |
| y | 650 | 5.0% |
| r | 628 | 4.8% |
| C | 616 | 4.8% |
| Other values (24) | 4483 |
| Distinct | 60 |
|---|---|
| Distinct (%) | 8.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| Biotech & Pharmaceuticals | |
|---|---|
| Insurance Carriers | |
| Computer Hardware & Software | |
| IT Services | |
| Health Care Services & Hospitals | |
| Other values (55) |
Length
| Max length | 40 |
|---|---|
| Median length | 35 |
| Mean length | 21.9083558 |
| Min length | 2 |
Characters and Unicode
| Total characters | 16256 |
|---|---|
| Distinct characters | 52 |
| Distinct categories | 6 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 11 ? |
|---|---|
| Unique (%) | 1.5% |
Sample
| 1st row | Aerospace & Defense |
|---|---|
| 2nd row | Health Care Services & Hospitals |
| 3rd row | Security Services |
| 4th row | Energy |
| 5th row | Advertising & Marketing |
Common Values
| Value | Count | Frequency (%) |
| Biotech & Pharmaceuticals | 112 | |
| Insurance Carriers | 63 | 8.5% |
| Computer Hardware & Software | 59 | 8.0% |
| IT Services | 50 | 6.7% |
| Health Care Services & Hospitals | 49 | 6.6% |
| Enterprise Software & Network Solutions | 42 | 5.7% |
| Internet | 29 | 3.9% |
| Consulting | 29 | 3.9% |
| Aerospace & Defense | 25 | 3.4% |
| Advertising & Marketing | 25 | 3.4% |
| Other values (50) | 259 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| 416 | ||
| services | 120 | 5.6% |
| biotech | 112 | 5.2% |
| pharmaceuticals | 112 | 5.2% |
| software | 101 | 4.7% |
| insurance | 69 | 3.2% |
| carriers | 63 | 2.9% |
| computer | 59 | 2.7% |
| hardware | 59 | 2.7% |
| health | 51 | 2.4% |
| Other values (101) | 986 |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 1784 | 11.0% |
| 1406 | 8.6% | |
| r | 1340 | 8.2% |
| a | 1218 | 7.5% |
| t | 1048 | 6.4% |
| i | 982 | 6.0% |
| s | 969 | 6.0% |
| n | 848 | 5.2% |
| c | 763 | 4.7% |
| o | 746 | 4.6% |
| Other values (42) | 5152 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 12614 | |
| Uppercase Letter | 1774 | 10.9% |
| Space Separator | 1406 | 8.6% |
| Other Punctuation | 430 | 2.6% |
| Decimal Number | 18 | 0.1% |
| Dash Punctuation | 14 | 0.1% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| e | 1784 | |
| r | 1340 | |
| a | 1218 | |
| t | 1048 | |
| i | 982 | |
| s | 969 | |
| n | 848 | 6.7% |
| c | 763 | 6.0% |
| o | 746 | 5.9% |
| u | 501 | 4.0% |
| Other values (15) | 2415 |
Uppercase Letter
| Value | Count | Frequency (%) |
| S | 313 | |
| C | 267 | |
| H | 159 | |
| I | 157 | |
| B | 151 | |
| P | 143 | |
| A | 98 | 5.5% |
| E | 79 | 4.5% |
| T | 78 | 4.4% |
| M | 73 | 4.1% |
| Other values (11) | 256 |
Other Punctuation
| Value | Count | Frequency (%) |
| & | 416 | |
| , | 14 | 3.3% |
Decimal Number
| Value | Count | Frequency (%) |
| 1 | 14 | |
| 2 | 4 | 22.2% |
Space Separator
| Value | Count | Frequency (%) |
| 1406 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 14 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 14388 | |
| Common | 1868 | 11.5% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| e | 1784 | |
| r | 1340 | 9.3% |
| a | 1218 | 8.5% |
| t | 1048 | 7.3% |
| i | 982 | 6.8% |
| s | 969 | 6.7% |
| n | 848 | 5.9% |
| c | 763 | 5.3% |
| o | 746 | 5.2% |
| u | 501 | 3.5% |
| Other values (36) | 4189 |
Common
| Value | Count | Frequency (%) |
| 1406 | ||
| & | 416 | 22.3% |
| , | 14 | 0.7% |
| 1 | 14 | 0.7% |
| - | 14 | 0.7% |
| 2 | 4 | 0.2% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 16256 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| e | 1784 | 11.0% |
| 1406 | 8.6% | |
| r | 1340 | 8.2% |
| a | 1218 | 7.5% |
| t | 1048 | 6.4% |
| i | 982 | 6.0% |
| s | 969 | 6.0% |
| n | 848 | 5.2% |
| c | 763 | 4.7% |
| o | 746 | 4.6% |
| Other values (42) | 5152 |
| Distinct | 25 |
|---|---|
| Distinct (%) | 3.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| Information Technology | |
|---|---|
| Biotech & Pharmaceuticals | |
| Business Services | |
| Insurance | |
| Health Care | |
| Other values (20) |
Length
| Max length | 34 |
|---|---|
| Median length | 28 |
| Mean length | 17.02695418 |
| Min length | 2 |
Characters and Unicode
| Total characters | 12634 |
|---|---|
| Distinct characters | 42 |
| Distinct categories | 6 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 2 ? |
|---|---|
| Unique (%) | 0.3% |
Sample
| 1st row | Aerospace & Defense |
|---|---|
| 2nd row | Health Care |
| 3rd row | Business Services |
| 4th row | Oil, Gas, Energy & Utilities |
| 5th row | Business Services |
Common Values
| Value | Count | Frequency (%) |
| Information Technology | 180 | |
| Biotech & Pharmaceuticals | 112 | |
| Business Services | 97 | |
| Insurance | 69 | 9.3% |
| Health Care | 49 | 6.6% |
| Finance | 42 | 5.7% |
| Manufacturing | 34 | 4.6% |
| Aerospace & Defense | 25 | 3.4% |
| Education | 23 | 3.1% |
| Retail | 15 | 2.0% |
| Other values (15) | 96 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| information | 180 | |
| technology | 180 | |
| 179 | ||
| biotech | 112 | 7.6% |
| pharmaceuticals | 112 | 7.6% |
| services | 101 | 6.9% |
| business | 97 | 6.6% |
| insurance | 69 | 4.7% |
| health | 49 | 3.3% |
| care | 49 | 3.3% |
| Other values (34) | 345 |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 1179 | 9.3% |
| n | 1091 | 8.6% |
| o | 969 | 7.7% |
| a | 943 | 7.5% |
| i | 856 | 6.8% |
| c | 843 | 6.7% |
| 731 | 5.8% | |
| s | 712 | 5.6% |
| r | 662 | 5.2% |
| t | 654 | 5.2% |
| Other values (32) | 3994 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 10367 | |
| Uppercase Letter | 1293 | 10.2% |
| Space Separator | 731 | 5.8% |
| Other Punctuation | 214 | 1.7% |
| Dash Punctuation | 19 | 0.2% |
| Decimal Number | 10 | 0.1% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| e | 1179 | |
| n | 1091 | |
| o | 969 | |
| a | 943 | |
| i | 856 | |
| c | 843 | |
| s | 712 | 6.9% |
| r | 662 | 6.4% |
| t | 654 | 6.3% |
| h | 453 | 4.4% |
| Other values (9) | 2005 |
Uppercase Letter
| Value | Count | Frequency (%) |
| I | 249 | |
| T | 210 | |
| B | 209 | |
| P | 121 | |
| S | 101 | |
| C | 56 | 4.3% |
| E | 49 | 3.8% |
| H | 49 | 3.8% |
| M | 49 | 3.8% |
| F | 43 | 3.3% |
| Other values (8) | 157 |
Other Punctuation
| Value | Count | Frequency (%) |
| & | 179 | |
| , | 35 | 16.4% |
Space Separator
| Value | Count | Frequency (%) |
| 731 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 19 |
Decimal Number
| Value | Count | Frequency (%) |
| 1 | 10 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 11660 | |
| Common | 974 | 7.7% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| e | 1179 | 10.1% |
| n | 1091 | 9.4% |
| o | 969 | 8.3% |
| a | 943 | 8.1% |
| i | 856 | 7.3% |
| c | 843 | 7.2% |
| s | 712 | 6.1% |
| r | 662 | 5.7% |
| t | 654 | 5.6% |
| h | 453 | 3.9% |
| Other values (27) | 3298 |
Common
| Value | Count | Frequency (%) |
| 731 | ||
| & | 179 | 18.4% |
| , | 35 | 3.6% |
| - | 19 | 2.0% |
| 1 | 10 | 1.0% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 12634 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| e | 1179 | 9.3% |
| n | 1091 | 8.6% |
| o | 969 | 7.7% |
| a | 943 | 7.5% |
| i | 856 | 6.8% |
| c | 843 | 6.7% |
| 731 | 5.8% | |
| s | 712 | 5.6% |
| r | 662 | 5.2% |
| t | 654 | 5.2% |
| Other values (32) | 3994 |
| Distinct | 13 |
|---|---|
| Distinct (%) | 1.8% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| Unknown / Non-Applicable | |
|---|---|
| $10+ billion (USD) | |
| $100 to $500 million (USD) | |
| $1 to $2 billion (USD) | |
| $500 million to $1 billion (USD) | |
| Other values (8) |
Length
| Max length | 32 |
|---|---|
| Median length | 26 |
| Mean length | 23.5916442 |
| Min length | 18 |
Characters and Unicode
| Total characters | 17505 |
|---|---|
| Distinct characters | 32 |
| Distinct categories | 10 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | $50 to $100 million (USD) |
|---|---|
| 2nd row | $2 to $5 billion (USD) |
| 3rd row | $100 to $500 million (USD) |
| 4th row | $500 million to $1 billion (USD) |
| 5th row | Unknown / Non-Applicable |
Common Values
| Value | Count | Frequency (%) |
| Unknown / Non-Applicable | 204 | |
| $10+ billion (USD) | 124 | |
| $100 to $500 million (USD) | 91 | |
| $1 to $2 billion (USD) | 60 | 8.1% |
| $500 million to $1 billion (USD) | 57 | 7.7% |
| $50 to $100 million (USD) | 46 | 6.2% |
| $25 to $50 million (USD) | 40 | 5.4% |
| $2 to $5 billion (USD) | 39 | 5.3% |
| $10 to $25 million (USD) | 32 | 4.3% |
| $5 to $10 billion (USD) | 19 | 2.6% |
| Other values (3) | 30 | 4.0% |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| usd | 538 | |
| to | 410 | |
| billion | 299 | |
| million | 296 | |
| unknown | 204 | 6.6% |
| 204 | 6.6% | |
| non-applicable | 204 | 6.6% |
| 10 | 193 | 6.2% |
| 500 | 148 | 4.8% |
| 100 | 137 | 4.4% |
| Other values (7) | 478 |
Most occurring characters
| Value | Count | Frequency (%) |
| 2369 | ||
| l | 1598 | 9.1% |
| n | 1415 | 8.1% |
| o | 1413 | 8.1% |
| i | 1394 | 8.0% |
| $ | 948 | 5.4% |
| 0 | 849 | 4.9% |
| U | 742 | 4.2% |
| D | 538 | 3.1% |
| ) | 538 | 3.1% |
| Other values (22) | 5701 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 8481 | |
| Space Separator | 2369 | 13.5% |
| Uppercase Letter | 2230 | 12.7% |
| Decimal Number | 1869 | 10.7% |
| Currency Symbol | 948 | 5.4% |
| Close Punctuation | 538 | 3.1% |
| Open Punctuation | 538 | 3.1% |
| Dash Punctuation | 204 | 1.2% |
| Other Punctuation | 204 | 1.2% |
| Math Symbol | 124 | 0.7% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| l | 1598 | |
| n | 1415 | |
| o | 1413 | |
| i | 1394 | |
| b | 503 | 5.9% |
| t | 414 | 4.9% |
| p | 408 | 4.8% |
| m | 296 | 3.5% |
| e | 208 | 2.5% |
| a | 208 | 2.5% |
| Other values (5) | 624 | 7.4% |
Uppercase Letter
| Value | Count | Frequency (%) |
| U | 742 | |
| D | 538 | |
| S | 538 | |
| A | 204 | 9.1% |
| N | 204 | 9.1% |
| L | 4 | 0.2% |
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 849 | |
| 1 | 459 | |
| 5 | 390 | |
| 2 | 171 | 9.1% |
Space Separator
| Value | Count | Frequency (%) |
| 2369 |
Currency Symbol
| Value | Count | Frequency (%) |
| $ | 948 |
Close Punctuation
| Value | Count | Frequency (%) |
| ) | 538 |
Open Punctuation
| Value | Count | Frequency (%) |
| ( | 538 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 204 |
Other Punctuation
| Value | Count | Frequency (%) |
| / | 204 |
Math Symbol
| Value | Count | Frequency (%) |
| + | 124 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 10711 | |
| Common | 6794 |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| l | 1598 | |
| n | 1415 | |
| o | 1413 | |
| i | 1394 | |
| U | 742 | |
| D | 538 | 5.0% |
| S | 538 | 5.0% |
| b | 503 | 4.7% |
| t | 414 | 3.9% |
| p | 408 | 3.8% |
| Other values (11) | 1748 |
Common
| Value | Count | Frequency (%) |
| 2369 | ||
| $ | 948 | |
| 0 | 849 | 12.5% |
| ) | 538 | 7.9% |
| ( | 538 | 7.9% |
| 1 | 459 | 6.8% |
| 5 | 390 | 5.7% |
| - | 204 | 3.0% |
| / | 204 | 3.0% |
| 2 | 171 | 2.5% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 17505 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 2369 | ||
| l | 1598 | 9.1% |
| n | 1415 | 8.1% |
| o | 1413 | 8.1% |
| i | 1394 | 8.0% |
| $ | 948 | 5.4% |
| 0 | 849 | 4.9% |
| U | 742 | 4.2% |
| D | 538 | 3.1% |
| ) | 538 | 3.1% |
| Other values (22) | 5701 |
| Distinct | 128 |
|---|---|
| Distinct (%) | 17.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| -1 | |
|---|---|
| Novartis, Baxter, Pfizer | 14 |
| Oak Ridge National Laboratory, National Renewable Energy Lab, Los Alamos National Laboratory | 12 |
| Travelers, Allstate, State Farm | 10 |
| Roche, GlaxoSmithKline, Novartis | 9 |
| Other values (123) |
Length
| Max length | 92 |
|---|---|
| Median length | 2 |
| Mean length | 15.97439353 |
| Min length | 2 |
Characters and Unicode
| Total characters | 11853 |
|---|---|
| Distinct characters | 63 |
| Distinct categories | 9 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 54 ? |
|---|---|
| Unique (%) | 7.3% |
Sample
| 1st row | -1 |
|---|---|
| 2nd row | -1 |
| 3rd row | -1 |
| 4th row | Oak Ridge National Laboratory, National Renewable Energy Lab, Los Alamos National Laboratory |
| 5th row | Commerce Signals, Cardlytics, Yodlee |
Common Values
| Value | Count | Frequency (%) |
| -1 | 460 | |
| Novartis, Baxter, Pfizer | 14 | 1.9% |
| Oak Ridge National Laboratory, National Renewable Energy Lab, Los Alamos National Laboratory | 12 | 1.6% |
| Travelers, Allstate, State Farm | 10 | 1.3% |
| Roche, GlaxoSmithKline, Novartis | 9 | 1.2% |
| Battelle, General Atomics, SAIC | 8 | 1.1% |
| Expedia Group, Orbitz Worldwide, Priceline.com | 7 | 0.9% |
| Pitney Bowes | 6 | 0.8% |
| Leidos, CACI International, Booz Allen Hamilton | 6 | 0.8% |
| FLURRY, Chartboost | 6 | 0.8% |
| Other values (118) | 204 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| 1 | 460 | 25.5% |
| national | 44 | 2.4% |
| laboratory | 28 | 1.6% |
| novartis | 25 | 1.4% |
| pfizer | 18 | 1.0% |
| group | 18 | 1.0% |
| 16 | 0.9% | |
| los | 15 | 0.8% |
| alamos | 15 | 0.8% |
| glaxosmithkline | 15 | 0.8% |
| Other values (433) | 1152 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1064 | 9.0% | |
| e | 940 | 7.9% |
| a | 822 | 6.9% |
| o | 672 | 5.7% |
| t | 654 | 5.5% |
| r | 634 | 5.3% |
| i | 626 | 5.3% |
| n | 557 | 4.7% |
| , | 500 | 4.2% |
| l | 490 | 4.1% |
| Other values (53) | 4894 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 7627 | |
| Uppercase Letter | 1682 | 14.2% |
| Space Separator | 1064 | 9.0% |
| Other Punctuation | 541 | 4.6% |
| Dash Punctuation | 467 | 3.9% |
| Decimal Number | 462 | 3.9% |
| Math Symbol | 4 | < 0.1% |
| Open Punctuation | 3 | < 0.1% |
| Close Punctuation | 3 | < 0.1% |
Most frequent character per category
Uppercase Letter
| Value | Count | Frequency (%) |
| S | 156 | 9.3% |
| A | 156 | 9.3% |
| C | 144 | 8.6% |
| N | 118 | 7.0% |
| L | 113 | 6.7% |
| T | 99 | 5.9% |
| R | 94 | 5.6% |
| I | 86 | 5.1% |
| B | 86 | 5.1% |
| M | 74 | 4.4% |
| Other values (16) | 556 |
Lowercase Letter
| Value | Count | Frequency (%) |
| e | 940 | |
| a | 822 | |
| o | 672 | |
| t | 654 | |
| r | 634 | |
| i | 626 | |
| n | 557 | 7.3% |
| l | 490 | 6.4% |
| s | 400 | 5.2% |
| c | 289 | 3.8% |
| Other values (15) | 1543 |
Other Punctuation
| Value | Count | Frequency (%) |
| , | 500 | |
| . | 19 | 3.5% |
| & | 13 | 2.4% |
| ' | 9 | 1.7% |
Decimal Number
| Value | Count | Frequency (%) |
| 1 | 460 | |
| 9 | 2 | 0.4% |
Math Symbol
| Value | Count | Frequency (%) |
| | | 2 | |
| + | 2 |
Space Separator
| Value | Count | Frequency (%) |
| 1064 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 467 |
Open Punctuation
| Value | Count | Frequency (%) |
| ( | 3 |
Close Punctuation
| Value | Count | Frequency (%) |
| ) | 3 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 9309 | |
| Common | 2544 | 21.5% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| e | 940 | 10.1% |
| a | 822 | 8.8% |
| o | 672 | 7.2% |
| t | 654 | 7.0% |
| r | 634 | 6.8% |
| i | 626 | 6.7% |
| n | 557 | 6.0% |
| l | 490 | 5.3% |
| s | 400 | 4.3% |
| c | 289 | 3.1% |
| Other values (41) | 3225 |
Common
| Value | Count | Frequency (%) |
| 1064 | ||
| , | 500 | |
| - | 467 | |
| 1 | 460 | |
| . | 19 | 0.7% |
| & | 13 | 0.5% |
| ' | 9 | 0.4% |
| ( | 3 | 0.1% |
| ) | 3 | 0.1% |
| | | 2 | 0.1% |
| Other values (2) | 4 | 0.2% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 11853 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 1064 | 9.0% | |
| e | 940 | 7.9% |
| a | 822 | 6.9% |
| o | 672 | 5.7% |
| t | 654 | 5.5% |
| r | 634 | 5.3% |
| i | 626 | 5.3% |
| n | 557 | 4.7% |
| , | 500 | 4.2% |
| l | 490 | 4.1% |
| Other values (53) | 4894 |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 | 24 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 718 | |
| 1 | 24 | 3.2% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 718 | |
| 1 | 24 | 3.2% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 718 | |
| 1 | 24 | 3.2% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 718 | |
| 1 | 24 | 3.2% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 718 | |
| 1 | 24 | 3.2% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 718 | |
| 1 | 24 | 3.2% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 | 17 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 725 | |
| 1 | 17 | 2.3% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 725 | |
| 1 | 17 | 2.3% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 725 | |
| 1 | 17 | 2.3% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 725 | |
| 1 | 17 | 2.3% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 725 | |
| 1 | 17 | 2.3% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 725 | |
| 1 | 17 | 2.3% |
| Distinct | 113 |
|---|---|
| Distinct (%) | 15.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 74.75471698 |
| Minimum | 15 |
|---|---|
| Maximum | 202 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 5.9 KiB |
Quantile statistics
| Minimum | 15 |
|---|---|
| 5-th percentile | 35.05 |
| Q1 | 52 |
| median | 69.5 |
| Q3 | 91 |
| 95-th percentile | 127 |
| Maximum | 202 |
| Range | 187 |
| Interquartile range (IQR) | 39 |
Descriptive statistics
| Standard deviation | 30.94589241 |
|---|---|
| Coefficient of variation (CV) | 0.4139657491 |
| Kurtosis | 1.967121091 |
| Mean | 74.75471698 |
| Median Absolute Deviation (MAD) | 19.5 |
| Skewness | 1.113064296 |
| Sum | 55468 |
| Variance | 957.6482571 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 43 | 22 | 3.0% |
| 65 | 20 | 2.7% |
| 61 | 18 | 2.4% |
| 80 | 18 | 2.4% |
| 52 | 18 | 2.4% |
| 49 | 18 | 2.4% |
| 81 | 17 | 2.3% |
| 74 | 16 | 2.2% |
| 63 | 16 | 2.2% |
| 56 | 16 | 2.2% |
| Other values (103) | 563 |
| Value | Count | Frequency (%) |
| 15 | 1 | 0.1% |
| 20 | 3 | 0.4% |
| 26 | 1 | 0.1% |
| 27 | 2 | 0.3% |
| 29 | 1 | 0.1% |
| 31 | 8 | |
| 32 | 6 | |
| 33 | 3 | 0.4% |
| 34 | 5 | |
| 35 | 8 |
| Value | Count | Frequency (%) |
| 202 | 3 | |
| 200 | 3 | |
| 190 | 3 | |
| 176 | 1 | 0.1% |
| 171 | 1 | 0.1% |
| 158 | 2 | 0.3% |
| 150 | 7 | |
| 139 | 3 | |
| 138 | 3 | |
| 136 | 1 | 0.1% |
| Distinct | 162 |
|---|---|
| Distinct (%) | 21.8% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 128.2142857 |
| Minimum | 16 |
|---|---|
| Maximum | 306 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 5.9 KiB |
Quantile statistics
| Minimum | 16 |
|---|---|
| 5-th percentile | 62 |
| Q1 | 96 |
| median | 124 |
| Q3 | 155 |
| 95-th percentile | 208 |
| Maximum | 306 |
| Range | 290 |
| Interquartile range (IQR) | 59 |
Descriptive statistics
| Standard deviation | 45.12864994 |
|---|---|
| Coefficient of variation (CV) | 0.3519783282 |
| Kurtosis | 0.6122000821 |
| Mean | 128.2142857 |
| Median Absolute Deviation (MAD) | 29 |
| Skewness | 0.6330608288 |
| Sum | 95135 |
| Variance | 2036.595045 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 140 | 16 | 2.2% |
| 119 | 15 | 2.0% |
| 110 | 15 | 2.0% |
| 124 | 15 | 2.0% |
| 113 | 13 | 1.8% |
| 127 | 13 | 1.8% |
| 86 | 12 | 1.6% |
| 173 | 12 | 1.6% |
| 101 | 12 | 1.6% |
| 139 | 11 | 1.5% |
| Other values (152) | 608 |
| Value | Count | Frequency (%) |
| 16 | 1 | 0.1% |
| 35 | 2 | 0.3% |
| 39 | 1 | 0.1% |
| 48 | 2 | 0.3% |
| 49 | 2 | 0.3% |
| 50 | 1 | 0.1% |
| 52 | 5 | |
| 55 | 3 | |
| 57 | 3 | |
| 58 | 3 |
| Value | Count | Frequency (%) |
| 306 | 3 | |
| 289 | 1 | 0.1% |
| 275 | 1 | 0.1% |
| 272 | 1 | 0.1% |
| 250 | 2 | |
| 239 | 2 | |
| 238 | 2 | |
| 231 | 1 | 0.1% |
| 228 | 2 | |
| 224 | 3 |
| Distinct | 219 |
|---|---|
| Distinct (%) | 29.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 101.4845013 |
| Minimum | 15.5 |
|---|---|
| Maximum | 254 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 5.9 KiB |
Quantile statistics
| Minimum | 15.5 |
|---|---|
| 5-th percentile | 50 |
| Q1 | 73.5 |
| median | 97.5 |
| Q3 | 122.5 |
| 95-th percentile | 167.5 |
| Maximum | 254 |
| Range | 238.5 |
| Interquartile range (IQR) | 49 |
Descriptive statistics
| Standard deviation | 37.48244883 |
|---|---|
| Coefficient of variation (CV) | 0.3693416071 |
| Kurtosis | 0.9866909301 |
| Mean | 101.4845013 |
| Median Absolute Deviation (MAD) | 24.5 |
| Skewness | 0.7964180441 |
| Sum | 75301.5 |
| Variance | 1404.93397 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 87.5 | 12 | 1.6% |
| 140 | 11 | 1.5% |
| 81 | 11 | 1.5% |
| 85 | 10 | 1.3% |
| 107.5 | 10 | 1.3% |
| 56.5 | 10 | 1.3% |
| 84.5 | 10 | 1.3% |
| 107 | 10 | 1.3% |
| 87 | 9 | 1.2% |
| 120 | 9 | 1.2% |
| Other values (209) | 640 |
| Value | Count | Frequency (%) |
| 15.5 | 1 | |
| 27.5 | 2 | |
| 29.5 | 1 | |
| 37.5 | 2 | |
| 39.5 | 1 | |
| 40.5 | 1 | |
| 41.5 | 1 | |
| 42 | 2 | |
| 43 | 2 | |
| 44 | 2 |
| Value | Count | Frequency (%) |
| 254 | 3 | |
| 237.5 | 1 | 0.1% |
| 232.5 | 1 | 0.1% |
| 225 | 2 | |
| 221.5 | 1 | 0.1% |
| 205 | 3 | |
| 194.5 | 2 | |
| 194 | 2 | |
| 184.5 | 2 | |
| 181 | 3 |
| Distinct | 343 |
|---|---|
| Distinct (%) | 46.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| MassMutual | 14 |
|---|---|
| Reynolds American | 14 |
| Takeda Pharmaceuticals | 14 |
| Software Engineering Institute | 11 |
| PNNL | 10 |
| Other values (338) |
Length
| Max length | 51 |
|---|---|
| Median length | 33 |
| Mean length | 15.22506739 |
| Min length | 2 |
Characters and Unicode
| Total characters | 11297 |
|---|---|
| Distinct characters | 70 |
| Distinct categories | 7 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 161 ? |
|---|---|
| Unique (%) | 21.7% |
Sample
| 1st row | Tecolote Research |
|---|---|
| 2nd row | University of Maryland Medical System |
| 3rd row | KnowBe4 |
| 4th row | PNNL |
| 5th row | Affinity Solutions |
Common Values
| Value | Count | Frequency (%) |
| MassMutual | 14 | 1.9% |
| Reynolds American | 14 | 1.9% |
| Takeda Pharmaceuticals | 14 | 1.9% |
| Software Engineering Institute | 11 | 1.5% |
| PNNL | 10 | 1.3% |
| Liberty Mutual Insurance | 10 | 1.3% |
| AstraZeneca | 9 | 1.2% |
| MITRE | 8 | 1.1% |
| Numeric, LLC | 7 | 0.9% |
| Advanced BioScience Laboratories | 7 | 0.9% |
| Other values (333) | 638 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| health | 36 | 2.4% |
| solutions | 22 | 1.5% |
| insurance | 22 | 1.5% |
| pharmaceuticals | 21 | 1.4% |
| the | 21 | 1.4% |
| inc | 21 | 1.4% |
| llc | 18 | 1.2% |
| of | 18 | 1.2% |
| institute | 16 | 1.1% |
| group | 15 | 1.0% |
| Other values (508) | 1299 |
Most occurring characters
| Value | Count | Frequency (%) |
| e | 1019 | 9.0% |
| a | 891 | 7.9% |
| 767 | 6.8% | |
| n | 688 | 6.1% |
| t | 685 | 6.1% |
| i | 677 | 6.0% |
| r | 657 | 5.8% |
| o | 624 | 5.5% |
| s | 595 | 5.3% |
| c | 408 | 3.6% |
| Other values (60) | 4286 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 8423 | |
| Uppercase Letter | 1950 | 17.3% |
| Space Separator | 767 | 6.8% |
| Other Punctuation | 81 | 0.7% |
| Decimal Number | 54 | 0.5% |
| Dash Punctuation | 18 | 0.2% |
| Math Symbol | 4 | < 0.1% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| e | 1019 | |
| a | 891 | |
| n | 688 | 8.2% |
| t | 685 | 8.1% |
| i | 677 | 8.0% |
| r | 657 | 7.8% |
| o | 624 | 7.4% |
| s | 595 | 7.1% |
| c | 408 | 4.8% |
| l | 408 | 4.8% |
| Other values (16) | 1771 |
Uppercase Letter
| Value | Count | Frequency (%) |
| C | 192 | 9.8% |
| S | 178 | 9.1% |
| T | 151 | 7.7% |
| A | 148 | 7.6% |
| I | 135 | 6.9% |
| L | 122 | 6.3% |
| M | 117 | 6.0% |
| P | 111 | 5.7% |
| R | 109 | 5.6% |
| E | 94 | 4.8% |
| Other values (16) | 593 |
Decimal Number
| Value | Count | Frequency (%) |
| 2 | 20 | |
| 0 | 10 | |
| 3 | 7 | 13.0% |
| 4 | 6 | 11.1% |
| 1 | 5 | 9.3% |
| 9 | 2 | 3.7% |
| 6 | 2 | 3.7% |
| 8 | 1 | 1.9% |
| 7 | 1 | 1.9% |
Other Punctuation
| Value | Count | Frequency (%) |
| . | 37 | |
| , | 21 | |
| & | 13 | 16.0% |
| ' | 8 | 9.9% |
| / | 2 | 2.5% |
Math Symbol
| Value | Count | Frequency (%) |
| < | 2 | |
| > | 2 |
Space Separator
| Value | Count | Frequency (%) |
| 767 |
Dash Punctuation
| Value | Count | Frequency (%) |
| - | 18 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 10373 | |
| Common | 924 | 8.2% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| e | 1019 | 9.8% |
| a | 891 | 8.6% |
| n | 688 | 6.6% |
| t | 685 | 6.6% |
| i | 677 | 6.5% |
| r | 657 | 6.3% |
| o | 624 | 6.0% |
| s | 595 | 5.7% |
| c | 408 | 3.9% |
| l | 408 | 3.9% |
| Other values (42) | 3721 |
Common
| Value | Count | Frequency (%) |
| 767 | ||
| . | 37 | 4.0% |
| , | 21 | 2.3% |
| 2 | 20 | 2.2% |
| - | 18 | 1.9% |
| & | 13 | 1.4% |
| 0 | 10 | 1.1% |
| ' | 8 | 0.9% |
| 3 | 7 | 0.8% |
| 4 | 6 | 0.6% |
| Other values (8) | 17 | 1.8% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 11297 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| e | 1019 | 9.0% |
| a | 891 | 7.9% |
| 767 | 6.8% | |
| n | 688 | 6.1% |
| t | 685 | 6.1% |
| i | 677 | 6.0% |
| r | 657 | 5.8% |
| o | 624 | 5.5% |
| s | 595 | 5.3% |
| c | 408 | 3.6% |
| Other values (60) | 4286 |
| Distinct | 37 |
|---|---|
| Distinct (%) | 5.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| CA | |
|---|---|
| MA | |
| NY | |
| VA | |
| IL | |
| Other values (32) |
Length
| Max length | 2 |
|---|---|
| Median length | 2 |
| Mean length | 2 |
| Min length | 2 |
Characters and Unicode
| Total characters | 1484 |
|---|---|
| Distinct characters | 24 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 2 ? |
|---|---|
| Unique (%) | 0.3% |
Sample
| 1st row | NM |
|---|---|
| 2nd row | MD |
| 3rd row | FL |
| 4th row | WA |
| 5th row | NY |
Common Values
| Value | Count | Frequency (%) |
| CA | 152 | |
| MA | 103 | |
| NY | 72 | 9.7% |
| VA | 41 | 5.5% |
| IL | 40 | 5.4% |
| MD | 35 | 4.7% |
| PA | 33 | 4.4% |
| TX | 28 | 3.8% |
| NC | 21 | 2.8% |
| WA | 21 | 2.8% |
| Other values (27) | 196 |
Length
Histogram of lengths of the category
| Value | Count | Frequency (%) |
| ca | 152 | |
| ma | 103 | |
| ny | 72 | 9.7% |
| va | 41 | 5.5% |
| il | 40 | 5.4% |
| md | 35 | 4.7% |
| pa | 33 | 4.4% |
| tx | 28 | 3.8% |
| wa | 21 | 2.8% |
| nc | 21 | 2.8% |
| Other values (27) | 196 |
Most occurring characters
| Value | Count | Frequency (%) |
| A | 382 | |
| C | 201 | |
| M | 158 | |
| N | 142 | 9.6% |
| Y | 78 | 5.3% |
| I | 74 | 5.0% |
| L | 68 | 4.6% |
| T | 56 | 3.8% |
| D | 54 | 3.6% |
| V | 41 | 2.8% |
| Other values (14) | 230 |
Most occurring categories
| Value | Count | Frequency (%) |
| Uppercase Letter | 1484 |
Most frequent character per category
Uppercase Letter
| Value | Count | Frequency (%) |
| A | 382 | |
| C | 201 | |
| M | 158 | |
| N | 142 | 9.6% |
| Y | 78 | 5.3% |
| I | 74 | 5.0% |
| L | 68 | 4.6% |
| T | 56 | 3.8% |
| D | 54 | 3.6% |
| V | 41 | 2.8% |
| Other values (14) | 230 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 1484 |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| A | 382 | |
| C | 201 | |
| M | 158 | |
| N | 142 | 9.6% |
| Y | 78 | 5.3% |
| I | 74 | 5.0% |
| L | 68 | 4.6% |
| T | 56 | 3.8% |
| D | 54 | 3.6% |
| V | 41 | 2.8% |
| Other values (14) | 230 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 1484 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| A | 382 | |
| C | 201 | |
| M | 158 | |
| N | 142 | 9.6% |
| Y | 78 | 5.3% |
| I | 74 | 5.0% |
| L | 68 | 4.6% |
| T | 56 | 3.8% |
| D | 54 | 3.6% |
| V | 41 | 2.8% |
| Other values (14) | 230 |
| Distinct | 102 |
|---|---|
| Distinct (%) | 13.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 47.52425876 |
| Minimum | -1 |
|---|---|
| Maximum | 277 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 50 |
| Negative (%) | 6.7% |
| Memory size | 5.9 KiB |
Quantile statistics
| Minimum | -1 |
|---|---|
| 5-th percentile | -1 |
| Q1 | 12 |
| median | 25 |
| Q3 | 60 |
| 95-th percentile | 170 |
| Maximum | 277 |
| Range | 278 |
| Interquartile range (IQR) | 48 |
Descriptive statistics
| Standard deviation | 53.83907976 |
|---|---|
| Coefficient of variation (CV) | 1.132875739 |
| Kurtosis | 2.777736511 |
| Mean | 47.52425876 |
| Median Absolute Deviation (MAD) | 17 |
| Skewness | 1.781382155 |
| Sum | 35263 |
| Variance | 2898.646509 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| -1 | 50 | 6.7% |
| 11 | 32 | 4.3% |
| 13 | 31 | 4.2% |
| 25 | 27 | 3.6% |
| 15 | 24 | 3.2% |
| 9 | 21 | 2.8% |
| 10 | 19 | 2.6% |
| 63 | 18 | 2.4% |
| 14 | 18 | 2.4% |
| 37 | 18 | 2.4% |
| Other values (92) | 484 |
| Value | Count | Frequency (%) |
| -1 | 50 | |
| 2 | 2 | 0.3% |
| 4 | 12 | 1.6% |
| 5 | 5 | 0.7% |
| 6 | 16 | 2.2% |
| 7 | 13 | 1.8% |
| 8 | 15 | 2.0% |
| 9 | 21 | |
| 10 | 19 | 2.6% |
| 11 | 32 |
| Value | Count | Frequency (%) |
| 277 | 1 | 0.1% |
| 240 | 14 | |
| 209 | 1 | 0.1% |
| 191 | 4 | 0.5% |
| 175 | 2 | 0.3% |
| 172 | 7 | |
| 171 | 1 | 0.1% |
| 170 | 14 | |
| 169 | 5 | 0.7% |
| 165 | 2 | 0.3% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 1 | |
|---|---|
| 0 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 1 |
|---|---|
| 2nd row | 1 |
| 3rd row | 1 |
| 4th row | 1 |
| 5th row | 1 |
Common Values
| Value | Count | Frequency (%) |
| 1 | 392 | |
| 0 | 350 |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 1 | 392 | |
| 0 | 350 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 392 | |
| 0 | 350 |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 1 | 392 | |
| 0 | 350 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 1 | 392 | |
| 0 | 350 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 1 | 392 | |
| 0 | 350 |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 1 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 575 | |
| 1 | 167 | 22.5% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 575 | |
| 1 | 167 | 22.5% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 575 | |
| 1 | 167 | 22.5% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 575 | |
| 1 | 167 | 22.5% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 575 | |
| 1 | 167 | 22.5% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 575 | |
| 1 | 167 | 22.5% |
aws
Categorical
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 566 | |
| 1 | 176 | 23.7% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 566 | |
| 1 | 176 | 23.7% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 566 | |
| 1 | 176 | 23.7% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 566 | |
| 1 | 176 | 23.7% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 566 | |
| 1 | 176 | 23.7% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 566 | |
| 1 | 176 | 23.7% |
excel
Categorical
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 1 | |
|---|---|
| 0 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 1 |
|---|---|
| 2nd row | 0 |
| 3rd row | 1 |
| 4th row | 0 |
| 5th row | 1 |
Common Values
| Value | Count | Frequency (%) |
| 1 | 388 | |
| 0 | 354 |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 1 | 388 | |
| 0 | 354 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 388 | |
| 0 | 354 |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 1 | 388 | |
| 0 | 354 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 1 | 388 | |
| 0 | 354 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 1 | 388 | |
| 0 | 354 |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 1 | |
|---|---|
| 0 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 1 |
| 4th row | 0 |
| 5th row | 1 |
Common Values
| Value | Count | Frequency (%) |
| 1 | 380 | |
| 0 | 362 |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 1 | 380 | |
| 0 | 362 |
Most occurring characters
| Value | Count | Frequency (%) |
| 1 | 380 | |
| 0 | 362 |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 1 | 380 | |
| 0 | 362 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 1 | 380 | |
| 0 | 362 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 1 | 380 | |
| 0 | 362 |
sas
Categorical
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 | 66 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 1 |
|---|---|
| 2nd row | 0 |
| 3rd row | 1 |
| 4th row | 0 |
| 5th row | 1 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 676 | |
| 1 | 66 | 8.9% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 676 | |
| 1 | 66 | 8.9% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 676 | |
| 1 | 66 | 8.9% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 676 | |
| 1 | 66 | 8.9% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 676 | |
| 1 | 66 | 8.9% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 676 | |
| 1 | 66 | 8.9% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 | 29 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 713 | |
| 1 | 29 | 3.9% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 713 | |
| 1 | 29 | 3.9% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 713 | |
| 1 | 29 | 3.9% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 713 | |
| 1 | 29 | 3.9% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 713 | |
| 1 | 29 | 3.9% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 713 | |
| 1 | 29 | 3.9% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 | 39 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 703 | |
| 1 | 39 | 5.3% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 703 | |
| 1 | 39 | 5.3% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 703 | |
| 1 | 39 | 5.3% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 703 | |
| 1 | 39 | 5.3% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 703 | |
| 1 | 39 | 5.3% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 703 | |
| 1 | 39 | 5.3% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 | 54 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 688 | |
| 1 | 54 | 7.3% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 688 | |
| 1 | 54 | 7.3% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 688 | |
| 1 | 54 | 7.3% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 688 | |
| 1 | 54 | 7.3% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 688 | |
| 1 | 54 | 7.3% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 688 | |
| 1 | 54 | 7.3% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 670 | |
| 1 | 72 | 9.7% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 670 | |
| 1 | 72 | 9.7% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 670 | |
| 1 | 72 | 9.7% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 670 | |
| 1 | 72 | 9.7% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 670 | |
| 1 | 72 | 9.7% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 670 | |
| 1 | 72 | 9.7% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 618 | |
| 1 | 124 | 16.7% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 618 | |
| 1 | 124 | 16.7% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 618 | |
| 1 | 124 | 16.7% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 618 | |
| 1 | 124 | 16.7% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 618 | |
| 1 | 124 | 16.7% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 618 | |
| 1 | 124 | 16.7% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 1 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 594 | |
| 1 | 148 | 19.9% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 594 | |
| 1 | 148 | 19.9% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 594 | |
| 1 | 148 | 19.9% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 594 | |
| 1 | 148 | 19.9% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 594 | |
| 1 | 148 | 19.9% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 594 | |
| 1 | 148 | 19.9% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 | 56 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 1 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 686 | |
| 1 | 56 | 7.5% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 686 | |
| 1 | 56 | 7.5% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 686 | |
| 1 | 56 | 7.5% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 686 | |
| 1 | 56 | 7.5% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 686 | |
| 1 | 56 | 7.5% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 686 | |
| 1 | 56 | 7.5% |
flink
Categorical
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 | 10 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 732 | |
| 1 | 10 | 1.3% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 732 | |
| 1 | 10 | 1.3% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 732 | |
| 1 | 10 | 1.3% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 732 | |
| 1 | 10 | 1.3% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 732 | |
| 1 | 10 | 1.3% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 732 | |
| 1 | 10 | 1.3% |
mongo
Categorical
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 | 37 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 705 | |
| 1 | 37 | 5.0% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 705 | |
| 1 | 37 | 5.0% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 705 | |
| 1 | 37 | 5.0% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 705 | |
| 1 | 37 | 5.0% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 705 | |
| 1 | 37 | 5.0% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 705 | |
| 1 | 37 | 5.0% |
| Distinct | 2 |
|---|---|
| Distinct (%) | 0.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| 0 | |
|---|---|
| 1 | 14 |
Length
| Max length | 1 |
|---|---|
| Median length | 1 |
| Mean length | 1 |
| Min length | 1 |
Characters and Unicode
| Total characters | 742 |
|---|---|
| Distinct characters | 2 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | 0 |
|---|---|
| 2nd row | 0 |
| 3rd row | 0 |
| 4th row | 0 |
| 5th row | 0 |
Common Values
| Value | Count | Frequency (%) |
| 0 | 728 | |
| 1 | 14 | 1.9% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| 0 | 728 | |
| 1 | 14 | 1.9% |
Most occurring characters
| Value | Count | Frequency (%) |
| 0 | 728 | |
| 1 | 14 | 1.9% |
Most occurring categories
| Value | Count | Frequency (%) |
| Decimal Number | 742 |
Most frequent character per category
Decimal Number
| Value | Count | Frequency (%) |
| 0 | 728 | |
| 1 | 14 | 1.9% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Common | 742 |
Most frequent character per script
Common
| Value | Count | Frequency (%) |
| 0 | 728 | |
| 1 | 14 | 1.9% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 742 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| 0 | 728 | |
| 1 | 14 | 1.9% |
| Distinct | 10 |
|---|---|
| Distinct (%) | 1.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| data scientist | |
|---|---|
| other scientist | |
| data engineer | |
| analyst | |
| machine learning engineer | 22 |
| Other values (5) |
Length
| Max length | 30 |
|---|---|
| Median length | 25 |
| Mean length | 13.53504043 |
| Min length | 2 |
Characters and Unicode
| Total characters | 10043 |
|---|---|
| Distinct characters | 19 |
| Distinct categories | 3 ? |
| Distinct scripts | 2 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | data scientist |
|---|---|
| 2nd row | data scientist |
| 3rd row | data scientist |
| 4th row | data scientist |
| 5th row | data scientist |
Common Values
| Value | Count | Frequency (%) |
| data scientist | 313 | |
| other scientist | 143 | |
| data engineer | 119 | 16.0% |
| analyst | 101 | 13.6% |
| machine learning engineer | 22 | 3.0% |
| Data scientist project manager | 16 | 2.2% |
| na | 10 | 1.3% |
| data analitics | 8 | 1.1% |
| data modeler | 5 | 0.7% |
| director | 5 | 0.7% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| scientist | 472 | |
| data | 461 | |
| other | 143 | 10.1% |
| engineer | 141 | 9.9% |
| analyst | 101 | 7.1% |
| machine | 22 | 1.5% |
| learning | 22 | 1.5% |
| project | 16 | 1.1% |
| manager | 16 | 1.1% |
| na | 10 | 0.7% |
| Other values (3) | 18 | 1.3% |
Most occurring characters
| Value | Count | Frequency (%) |
| t | 1678 | |
| a | 1226 | |
| i | 1150 | |
| e | 1129 | |
| s | 1053 | |
| n | 955 | |
| 680 | ||
| c | 523 | 5.2% |
| d | 455 | 4.5% |
| r | 353 | 3.5% |
| Other values (9) | 841 |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 9347 | |
| Space Separator | 680 | 6.8% |
| Uppercase Letter | 16 | 0.2% |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| t | 1678 | |
| a | 1226 | |
| i | 1150 | |
| e | 1129 | |
| s | 1053 | |
| n | 955 | |
| c | 523 | 5.6% |
| d | 455 | 4.9% |
| r | 353 | 3.8% |
| g | 179 | 1.9% |
| Other values (7) | 646 | 6.9% |
Space Separator
| Value | Count | Frequency (%) |
| 680 |
Uppercase Letter
| Value | Count | Frequency (%) |
| D | 16 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 9363 | |
| Common | 680 | 6.8% |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| t | 1678 | |
| a | 1226 | |
| i | 1150 | |
| e | 1129 | |
| s | 1053 | |
| n | 955 | |
| c | 523 | 5.6% |
| d | 455 | 4.9% |
| r | 353 | 3.8% |
| g | 179 | 1.9% |
| Other values (8) | 662 | 7.1% |
Common
| Value | Count | Frequency (%) |
| 680 |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 10043 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| t | 1678 | |
| a | 1226 | |
| i | 1150 | |
| e | 1129 | |
| s | 1053 | |
| n | 955 | |
| 680 | ||
| c | 523 | 5.2% |
| d | 455 | 4.5% |
| r | 353 | 3.5% |
| Other values (9) | 841 |
seniority_by_title
Categorical
| Distinct | 3 |
|---|---|
| Distinct (%) | 0.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| na | |
|---|---|
| sr | |
| jr | 3 |
Length
| Max length | 2 |
|---|---|
| Median length | 2 |
| Mean length | 2 |
| Min length | 2 |
Characters and Unicode
| Total characters | 1484 |
|---|---|
| Distinct characters | 5 |
| Distinct categories | 1 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | na |
|---|---|
| 2nd row | na |
| 3rd row | na |
| 4th row | na |
| 5th row | na |
Common Values
| Value | Count | Frequency (%) |
| na | 519 | |
| sr | 220 | |
| jr | 3 | 0.4% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| na | 519 | |
| sr | 220 | |
| jr | 3 | 0.4% |
Most occurring characters
| Value | Count | Frequency (%) |
| n | 519 | |
| a | 519 | |
| r | 223 | |
| s | 220 | |
| j | 3 | 0.2% |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 1484 |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| n | 519 | |
| a | 519 | |
| r | 223 | |
| s | 220 | |
| j | 3 | 0.2% |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 1484 |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| n | 519 | |
| a | 519 | |
| r | 223 | |
| s | 220 | |
| j | 3 | 0.2% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 1484 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| n | 519 | |
| a | 519 | |
| r | 223 | |
| s | 220 | |
| j | 3 | 0.2% |
| Distinct | 3 |
|---|---|
| Distinct (%) | 0.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 5.9 KiB |
| na | |
|---|---|
| M | |
| P |
Length
| Max length | 2 |
|---|---|
| Median length | 2 |
| Mean length | 1.516172507 |
| Min length | 1 |
Characters and Unicode
| Total characters | 1125 |
|---|---|
| Distinct characters | 4 |
| Distinct categories | 2 ? |
| Distinct scripts | 1 ? |
| Distinct blocks | 1 ? |
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | M |
|---|---|
| 2nd row | M |
| 3rd row | M |
| 4th row | na |
| 5th row | na |
Common Values
| Value | Count | Frequency (%) |
| na | 383 | |
| M | 252 | |
| P | 107 | 14.4% |
Length
Histogram of lengths of the category
Category Frequency Plot
| Value | Count | Frequency (%) |
| na | 383 | |
| m | 252 | |
| p | 107 | 14.4% |
Most occurring characters
| Value | Count | Frequency (%) |
| n | 383 | |
| a | 383 | |
| M | 252 | |
| P | 107 | 9.5% |
Most occurring categories
| Value | Count | Frequency (%) |
| Lowercase Letter | 766 | |
| Uppercase Letter | 359 |
Most frequent character per category
Lowercase Letter
| Value | Count | Frequency (%) |
| n | 383 | |
| a | 383 |
Uppercase Letter
| Value | Count | Frequency (%) |
| M | 252 | |
| P | 107 |
Most occurring scripts
| Value | Count | Frequency (%) |
| Latin | 1125 |
Most frequent character per script
Latin
| Value | Count | Frequency (%) |
| n | 383 | |
| a | 383 | |
| M | 252 | |
| P | 107 | 9.5% |
Most occurring blocks
| Value | Count | Frequency (%) |
| ASCII | 1125 |
Most frequent character per block
ASCII
| Value | Count | Frequency (%) |
| n | 383 | |
| a | 383 | |
| M | 252 | |
| P | 107 | 9.5% |
Spearman's ρ
The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
Pearson's r
The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
Kendall's τ
Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
Cramér's V (φc)
Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.Phik (φk)
Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here. A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
First rows
| df_index | Job Title | Salary Estimate | Job Description | Rating | Company Name | Location | Headquarters | Size | Founded | Type of ownership | Industry | Sector | Revenue | Competitors | Hourly | Employer provided | Lower Salary | Upper Salary | Avg Salary(K) | company_txt | Job Location | Age | Python | spark | aws | excel | sql | sas | keras | pytorch | scikit | tensor | hadoop | tableau | bi | flink | mongo | google_an | job_title_sim | seniority_by_title | Degree | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Data Scientist | $53K-$91K (Glassdoor est.) | Data Scientist\nLocation: Albuquerque, NM\nEducation Required: Bachelor’s degree required, preferably in math, engineering, business, or the sciences.\nSkills Required:\nBachelor’s Degree in relevant field, e.g., math, data analysis, database, computer science, Artificial Intelligence (AI); three years’ experience credit for Master’s degree; five years’ experience credit for a Ph.D\nApplicant should be proficient in the use of Power BI, Tableau, Python, MATLAB, Microsoft Word, PowerPoint, Excel, and working knowledge of MS Access, LMS, SAS, data visualization tools, and have a strong algorithmic aptitude\nExcellent verbal and written communication skills, and quantitative analytical skills are required\nApplicant must be able to work in a team environment\nU.S. citizenship and ability to obtain a DoD Secret Clearance required\nResponsibilities: The applicant will be responsible for formulating analytical solutions to complex data problems; creating data analytic models to improve data metrics; analyzing customer behavior and trends; delivering insights to stakeholders, as well as designing and crafting reports, dashboards, models, and algorithms to make data insights actionable; selecting features, building and optimizing classifiers using machine learning techniques; data mining using state-of-the-art methods, extending organization’s data with third party sources of information when needed; enhancing data collection procedures to include information that is relevant for building analytic systems; processing, cleansing, and verifying the integrity of data used for analysis; doing ad-hoc analysis and presenting results in a clear manner; and creating automated anomaly detection systems and constant tracking of its performance.\nBenefits:\nWe offer competitive salaries commensurate with education and experience. We have an excellent benefits package that includes:\nComprehensive health, dental, life, long and short term disability insurance\n100% Company funded Retirement Plans\nGenerous vacation, holiday and sick pay plans\nTuition assistance\n\nBenefits are provided to employees regularly working a minimum of 30 hours per week.\n\nTecolote Research is a private, employee-owned corporation where people are our primary resource. Our investments in technology and training give our employees the tools to ensure our clients are provided the solutions they need, and our very high employee retention rate and stable workforce is an added value to our customers. Apply now to connect with a company that invests in you. | 3.8 | Tecolote Research\n3.8 | Albuquerque, NM | Goleta, CA | 501 - 1000 | 1973 | Company - Private | Aerospace & Defense | Aerospace & Defense | $50 to $100 million (USD) | -1 | 0 | 0 | 53 | 91 | 72.0 | Tecolote Research | NM | 48 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | data scientist | na | M |
| 1 | 1 | Healthcare Data Scientist | $63K-$112K (Glassdoor est.) | What You Will Do:\n\nI. General Summary\n\nThe Healthcare Data Scientist position will join our Advanced Analytics group at the University of Maryland Medical System (UMMS) in support of its strategic priority to become a data-driven and outcomes-oriented organization. The successful candidate will have 3+ years of experience with Machine Learning, Predictive Modeling, Statistical Analysis, Mathematical Optimization, Algorithm Development and a passion for working with healthcare data. Previous experience with various computational approaches along with an ability to demonstrate a portfolio of relevant prior projects is essential. This position will report to the UMMS Vice President for Enterprise Data and Analytics (ED&A).\n\nII. Principal Responsibilities and Tasks\n\n• Develops predictive and prescriptive analytic models in support of the organization’s clinical, operations and business initiatives and priorities.\n• Deploys solutions so that they provide actionable insights to the organization and are embedded or integrated with application systems\n• Supports and drives analytic efforts designed around organization’s strategic priorities and clinical/business problems\n• Works in a team to drive disruptive innovation, which may translate into improved quality of care, clinical outcomes, reduced costs, temporal efficiencies and process improvements.\n• Builds and extends our analytics portfolio supported by robust documentation\n• Works with autonomy to find solutions to complex problems using open source tools and in-house development\n• Stays abreast of state-of-the-art literature in the fields of operations research, statistical modeling, statistical process control and mathematical optimization\n• Creates, communicates, and manages the project plans and other required project documentation and provides updates to leadership as necessary\n• Develops and maintains relationships with business, IT and clinical leaders and stakeholders across the enterprise to facilitate collaboration and effective communication\n• Works with the analytics team and clinical/business stakeholders to develop pilots so that they may be tested and validated in pilot settings\n• Performs analysis to evaluate primary and secondary objectives from such pilots\n• Assists leadership with strategies for scaling successful projects across the organization and enhances the analytics applications based on feedback from end-users and clinical/business consumers\n• Assists leadership with dissemination of success stories (and failures) in an effort to increase analytics literacy and adoption across the organization.\n\nWhat You Need to Be Successful:\n\nIII. Education and Experience\n\n• Master’s or higher degree (may be substituted by relevant work experience) in applied mathematics, physics, computer science, engineering, statistics or a related field\n• 3+ years of Mathematical Optimization, Machine Learning, Predictive Analytics and Algorithm Development experience (experience with tools such as WEKA, RapidMiner, R. Python or other open source tools strongly desired)\n• Strong development skills in two or more of the following: C/C++, C#, Python, Java\n• Combining analytic methods with advanced data visualizations\n• Expert ability to breakdown and clearly define problems\n• Experience with Natural Language Processing preferred\n\nIV. Knowledge, Skills and Abilities\n\n• Proven communications skills – Effective at working independently and in collaboration with other staff members. Capable of clearly presenting findings orally, in writing, or through graphics.\n• Proven analytical skills – Able to compare, contrast, and validate work with keen attention to detail. Skilled in working with “real world” data including scrubbing, transformation, and imputation.\n• Proven problem solving skills – Able to plan work, set clear direction, and coordinate own tasks in a fast-paced multidisciplinary environment. Expert at triaging issues, identifying data anomalies, and debugging software.\n• Design and prototype new application functionality for our products.\n• Change oriented – actively generates process improvements; supports and drives change, and confronts difficult circumstances in creative ways\n• Effective communicator and change agent\n• Ability to prioritize the tasks of the project timeline to achieve the desired results\n• Strong analytic and problem solving skills\n• Ability to cooperatively and effectively work with people from various organization levels\n\nWe are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. | 3.4 | University of Maryland Medical System\n3.4 | Linthicum, MD | Baltimore, MD | 10000+ | 1984 | Other Organization | Health Care Services & Hospitals | Health Care | $2 to $5 billion (USD) | -1 | 0 | 0 | 63 | 112 | 87.5 | University of Maryland Medical System | MD | 37 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | data scientist | na | M |
| 2 | 2 | Data Scientist | $80K-$90K (Glassdoor est.) | KnowBe4, Inc. is a high growth information security company. We are the world's largest provider of new-school security awareness training and simulated phishing. KnowBe4 was created to help organizations manage the ongoing problem of social engineering. Tens of thousands of organizations worldwide use KnowBe4's platform to mobilize their end users as a last line of defense and enable them to make better security decisions, every day.\n\nWe are ranked #1 best place to work in technology nationwide by Fortune Magazine and have placed #1 or #2 in The Tampa Bay Top Workplaces Survey for the last four years. We also just had our 27th record-setting quarter in a row!\n\nThe Data Scientist will work closely with the VP of FP&A and the Quantitative Analytics Manager to implement advanced analytical models and other data-driven solutions.\n\nResponsibilities:\nWork with key stakeholders throughout the organization to identify opportunities using financial data to develop business solutions.\nDevelop new and enhance existing data collection procedures to ensure that all data relevant for analyses is captured.\nCleanse, consolidate, and verify the integrity of data used in analyses.\nBuild and validate predictive models to increase customer retention, revenue generation, and other business outcomes.\nDevelop relevant statistical models to assist with profitability forecasting\nCreate the analytics to leverage known, inferred and appended information about origins and recognizing patterns to assist in outlook forecasting\nAssist in the design and data modeling of data warehouse.\nVisualize data, especially in reports and dashboards, to communicate analysis results to stakeholders.\nExtend data collection to unstructured data within the company and external sources\nMine and collect data (both structured and unstructured) to detect patterns, opportunities and insights that drive our organization\nCreate and execute automation and data mining requests utilizing SQL, Access, Excel, SAS and other statistical programs\nTrouble shoot forecast and optimization anomalies with FP&A team through the use of statistical and mathematical optimization models. Develop testing to explain and or reduce these anomalies.\nOversee and develop key metric forecasts as well as provide budget support based on trends in the business/industry.\nMinimum Qualifications:\nMaster's degree in Statistics, Computer Science, Mathematics or other quantitative discipline required\n2-3 years of experience in similar role (Master's Degree)\n0-2 years of experience in similar role (PhD)\nExperience leveraging predictive modeling, big data analytics, exploratory data analysis and machine learning to drive significant business impact\nExperience with statistical computer languages (Python, R etc.) to manipulate and analyze large datasets preferred.\nExperience with data visualization tools like D3.js, matplotlib, etc., preferred\nExceptional understanding of machine learning algorithms such as Random Forest, SVM, k-NN, Naïve Bayes, Gradient Boosting a plus.\nApplied statistical skills including statistical testing, regression, etc.\nExperience with data bases, query languages, and associated data architecture.\nExperience with distributed computing tools (Hive, Spark, etc.) is a plus.\nStrong analytical skills and ability to meet project deadlines.\nNote: An applicant assessment, background check and drug test may be part of your hiring procedure.\n\nNo recruitment agencies, please. | 4.8 | KnowBe4\n4.8 | Clearwater, FL | Clearwater, FL | 501 - 1000 | 2010 | Company - Private | Security Services | Business Services | $100 to $500 million (USD) | -1 | 0 | 0 | 80 | 90 | 85.0 | KnowBe4 | FL | 11 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | data scientist | na | M |
| 3 | 3 | Data Scientist | $56K-$97K (Glassdoor est.) | *Organization and Job ID**\nJob ID: 310709\n\nDirectorate: Earth & Biological Sciences\n\nDivision: Biological Sciences\n\nGroup: Exposure Science Team\n*Job Description**\nThe Biological System Science (BSS) Group in the Biological Sciences Division of the Pacific Northwest National Laboratory (PNNL) is seeking a staff scientist with multidisciplinary experience in computational chemistry, cheminformatics, advanced statistics and/or machine learning/deep learning/AI. Preferred candidates will have a broad understanding of the state of computational metabolomics and experience in designing and implementing novel deep learning networks for chemistry applications. Research experience in drug design, cheminformatics, deep learning, machine learning and/or small molecule identification is also highly valued. Successful candidates will join a large, uniquely collaborative, collegial group of innovators driving the integration of data science, computational science and analytical chemistry to solve the nations most challenging problems in human health, chemical forensics, and national security. The BSS Group is diverse and inclusive, working closely with colleagues across the laboratory with expertise in computational biology, integrative omics, applied mathematics, computer science, and statistics.\n\n+ Apply knowledge of statistics, machine learning, advanced mathematics, simulation, software development, and data modeling to to design, development and implement methods that integrate, clean and analyze data, recognize patterns, address uncertainty, pose questions, and make discoveries from structured and/or unstructured data.\n\n+ Produce solutions driven by exploratory data analysis from complex and high-dimensional datasets.\n\n+ Design, develop, and evaluate predictive models and advanced algorithms that lead to optimal value extraction from data.\n\n+ Develop and maintain existing deep learning networks that generate novel molecules for drug discovery applications\n\n+ Contribue or author proposals, peer-reviewed papers, and other technical products.\n*Minimum Qualifications**\nBS/BA with 0-1 years of experience or MS/MA with 0-1 years of experience\n*Preferred Qualifications**\n+ MS in chemical engineering, computer science, or related field with a GPA of 3.5+ 5+ years of research experience\n\n+ Intermediate level programming experience (preferably Python) and high-performance computing experience\n\n+ At least one first author published, or proof of submitted, paper applying deep learning for use in novel compound generation\n\n+ Understanding of the NMDA receptor and potential drug targets\n\n+ Research experience in drug design, cheminformatics, deep learning, machine learning and/or small molecule identification\n*Equal Employment Opportunity**\nBattelle Memorial Institute (BMI) at Pacific Northwest National Laboratory (PNNL) is an Affirmative Action/Equal Opportunity Employer and supports diversity in the workplace. All employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, marital or family status, sexual orientation, gender identity, or genetic information. All BMI staff must be able to demonstrate the legal right to work in the United States. BMI is an E-Verify employer. Learn more at jobs.pnnl.gov.\n*_Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from participation in certain foreign government talent recruitment programs. If you are offered a position at PNNL and are currently a participant in a foreign government talent recruitment program you will be required to disclose this information before your first day of employment._**\n_Directorate:_ _Earth & Biological Sciences_\n\n_Job Category:_ _Scientists/Scientific Support_\n\n_Group:_ _Biological Systems Science_\n\n_Opening Date:_ _2020-03-26_\n\n_Closing Date:_ _2020-04-05_ | 3.8 | PNNL\n3.8 | Richland, WA | Richland, WA | 1001 - 5000 | 1965 | Government | Energy | Oil, Gas, Energy & Utilities | $500 million to $1 billion (USD) | Oak Ridge National Laboratory, National Renewable Energy Lab, Los Alamos National Laboratory | 0 | 0 | 56 | 97 | 76.5 | PNNL | WA | 56 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | data scientist | na | na |
| 4 | 4 | Data Scientist | $86K-$143K (Glassdoor est.) | Data Scientist\nAffinity Solutions / Marketing Cloud seeks smart, curious, technically savvy candidates to join our cutting-edge data science team. We hire the best and brightest and give them the opportunity to work on industry-leading technologies.\nThe data sciences team at AFS/Marketing Cloud build models, machine learning algorithms that power all our ad-tech/mar-tech products at scale, develop methodology and tools to precisely and effectively measure market campaign effects, and research in-house and public data sources for consumer spend behavior insights. In this role, you'll have the opportunity to come up with new ideas and solutions that will lead to improvement of our ability to target the right audience, derive insights and provide better measurement methodology for marketing campaigns. You'll access our core data asset and machine learning infrastructure to power your ideas.\nDuties and Responsibilities\n· Support all clients model building needs, including maintaining and improving current modeling/scoring methodology and processes,\n· Provide innovative solutions to customized modeling/scoring/targeting with appropriate ML/statistical tools,\n· Provide analytical/statistical support such as marketing test design, projection, campaign measurement, market insights to clients and stakeholders.\n· Mine large consumer datasets in the cloud environment to support ad hoc business and statistical analysis,\n· Develop and Improve automation capabilities to enable customized delivery of the analytical products to clients,\n· Communicate the methodologies and the results to the management, clients and none technical stakeholders.\nBasic Qualifications\n· Advanced degree in Statistics/Mathematics/Computer Science/Economics or other fields that requires advanced training in data analytics.\n· Being able to apply basic statistical/ML concepts and reasoning to address and solve business problems such as targeting, test design, KPI projection and performance measurement.\n· Entrepreneurial, highly self-motivated, collaborative, keen attention to detail, willingness and capable learn quickly, and ability to effectively prioritize and execute tasks in a high pressure environment.\n· Being flexible to accept different task assignments and able to work on a tight time schedule.\n· Excellent command of one or more programming languages; preferably Python, SAS or R\n· Familiar with one of the database technologies such as PostgreSQL, MySQL, can write basic SQL queries\n· Great communication skills (verbal, written and presentation)\nPreferred Qualifications\n· Experience or exposure to large consumer and/or demographic data sets.\n· Familiarity with data manipulation and cleaning routines and techniques. | 2.9 | Affinity Solutions\n2.9 | New York, NY | New York, NY | 51 - 200 | 1998 | Company - Private | Advertising & Marketing | Business Services | Unknown / Non-Applicable | Commerce Signals, Cardlytics, Yodlee | 0 | 0 | 86 | 143 | 114.5 | Affinity Solutions | NY | 23 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | data scientist | na | na |
| 5 | 5 | Data Scientist | $71K-$119K (Glassdoor est.) | CyrusOne is seeking a talented Data Scientist who holds a range of data-focused skills both in technical and analytical domains. The ideal candidate is adept at processing, cleansing, and verifying the integrity of data used for visualization and analysis. This role is dynamic, granting the candidate the opportunity to participate in a wide variety of projects and collaborate with many cross-functional teams throughout the business.\n\nDuties and Responsibilities:\nParticipate in an agile scrum cadence\nProcess, cleanse, and verify the integrity of data used for analysis\nPerform functional business requirements analysis and data analysis\nDevelop data models and algorithms to apply to data sets\nAugment data collection procedures to include necessary information for building accurate analytics\nCollaborate with stakeholders throughout the organization to identify opportunities for leveraging data to drive business solutions\nEvaluate the effectiveness and accuracy of data sources and data gathering techniques\nGather critical information from meetings with various stakeholders and produce useful reports\nCoordinate with cross-functional teams to implement models and monitor outcomes\nDevelop automated discrepancy detection systems and distribute reconciliation reports to stakeholders\nRequirements:\nMust be legally authorized to work in the United States for any employer without sponsorship\nProfessional experience using statistical software languages like R, Python, and SQL to query, manipulate, and draw insights from data sets\nStrong problem-solving skills with an emphasis on product development\nExtensive experience with Microsoft SQL, MySQL and MongoDB\nUnderstanding of version control (git) and project management with Azure DevOps\nKnowledge of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.)\nExperience visualizing data for stakeholders using visualization tools such as Power BI\nExperience working with and creating data architectures\nUnderstanding and adherence to agile principles and practices\nAbility to work on problems of any scope where the analysis of situations or data requires a review of a variety of factors\nSelf-maintainability and reliability with minimal supervision\nExcellent interpersonal communication, decision making, presentation, and organizational skills\nAbility to build productive internal/external working relationships\nHarmonious with CyrusOne culture, core values, and business goals\nMinimum Qualifications:\n2+ years of related experience in a data analyst role\nStrong can-do attitude in a time sensitive environment\nOther important information about this position:\nThis position requires typical weekday (Monday - Friday) attendance in an office setting, at times after hours work may be required to meet business and customer needs\nEvery position requires certain physical capabilities. CyrusOne seeks to make reasonable accommodations that enable individuals with disabilities to perform essential duties when possible\nCyrusOne is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, disability, veteran status, or other legally protected status.\n\nCyrusOne provides reasonable accommodation for qualified individuals with disabilities in accordance with the Americans with Disabilities Act (ADA) and any other state or local laws. We will respond to requests for reasonable accommodations to assist you in applying for positions at CyrusOne, or to submit a resume. If you need to request an accommodation, please contact our Human Resources at 214.488.1365 (Option 7) or by email at HR@cyrusone.com. | 3.4 | CyrusOne\n3.4 | Dallas, TX | Dallas, TX | 201 - 500 | 2000 | Company - Public | Real Estate | Real Estate | $1 to $2 billion (USD) | Digital Realty, CoreSite, Equinix | 0 | 0 | 71 | 119 | 95.0 | CyrusOne | TX | 21 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | data scientist | na | na |
| 6 | 6 | Data Scientist | $54K-$93K (Glassdoor est.) | Job Description\n\n**Please only local candidates apply - thank you**\n\nClearOne Advantage is a fast-growing company that is aggressively hiring due to increased business. We are always improving our marketing, culture and technology to provide our employees with the best work atmosphere and our customers with excellent customer service. COA’s proprietary software is tailored to our industry and allows the client to receive the best service possible.\n\nWe are looking for a Data Scientist to analyze large amounts of raw information to find patterns that will help improve our company. We will rely on you to build data products to extract valuable business insights. In this role, you should be highly analytical with a knack for analysis, math and statistics. Critical thinking and problem-solving skills are essential for interpreting data. We want to see a passion for machine-learning and research. Your goal will be to help our company analyze trends to make better decisions.\n\nIf you are looking to work in a team environment, a place where you are more a name than a number, where you interact with leadership daily, then please send your resume for review!\n\nPerks:\nGreat location, right on the water in the Canton Crossing Tower\nCasual work environment and WFH flexibility\nRoom for advancement\nWhat you'll be doing:\nIdentify valuable data sources and automate collection processes\nUndertake preprocessing of structured and unstructured data\nAnalyze large amounts of information to discover trends and patterns\nBuild predictive models and machine-learning algorithms\nCombine models through ensemble modeling\nPresent information using data visualization techniques\nPropose solutions and strategies to business challenges\nCollaborate with engineering and product development teams | 4.1 | ClearOne Advantage\n4.1 | Baltimore, MD | Baltimore, MD | 501 - 1000 | 2008 | Company - Private | Banks & Credit Unions | Finance | Unknown / Non-Applicable | -1 | 0 | 0 | 54 | 93 | 73.5 | ClearOne Advantage | MD | 13 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | data scientist | na | na |
| 7 | 7 | Data Scientist | $86K-$142K (Glassdoor est.) | Advanced Analytics – Lead Data Scientist\nOverview\n\n\nWe are looking for a Data Scientist to join our Data Science team to work on interesting projects to help our clients make data driven solutions. As a Data Scientist, you’ll work closely with the clients to understand their business needs, frame them as statistical problems, and solve them with cutting edge techniques. Collaborate with your team, including machine learning engineers, data engineers, analysts, and TPMs to define tasks, provide estimates, and work together to deliver a world class solution. The ideal candidate will have the balance of technical skills and business acumen to help the client better understand their core needs while understanding technical limitations.\n\nAbout you…\nExperience partnering & communicating with executive management team to understand business needs and pain points\nAbility to communicate data science concepts to business stakeholders\nPassion for the application of machine learning to real world problems\nAdept at developing and iterating solutions rapidly\nAbility to lead development of data science solutions\nWhat we offer our consultants:\n\nExperience working with both large enterprise clients and mid-sized clients\nProgressive responsibilities that encourage ownership and design\nOpportunity to learn and gain experience in complimentary skills such as meeting facilitation, data management, project management, data modeling, and data management\nCompany Culture that celebrates “Foster the culture of we”, “Act with integrity” and “Drive towards excellence” while having fun at work\nTraining and certification opportunities to support your career now and after Logic20/20\nVarious opportunities to give back to the community through company sponsored events\nRequired Qualifications\nExperience building machine learning models using Python\nExperience deploying machine learning models in a production environment\nStrong knowledge of probability statistics\nExperience with Tensorflow or PyTorch\nExperience writing SQL to query databases, structure and modify data\nDemonstrated ability to frame business problems as statistical problems and solve them\nAbility to work both independently and as part of a team\nExperience working in ambiguous and dynamic environments that move quickly\nAn undergraduate degree in mathematics, computer science, or engineering is preferred\nPreferred Qualifications\nPassion and experience driving adoption of machine learning in industry\nExperience deploying machine learning on large scales through Spark or other big data technology\nExperience building systems in AWS\nExperience in computer vision with deep neural networks\nExperience with leading workshops with executives to drive requirements gathering\nMasters or PhD in data science or related field\n\nAbout Logic20/20. . .\n\n\nLogic20/20 is one of Seattle’s fastest growing full-service consulting firms. Our core competency is creating simplicity and efficiency in complex solutions. Although we make it look like magic, we succeed by combining methodical and structured approaches with our substantial experience to design elegant solutions for even the most intricate challenges. Our rapid growth is in response to our ability to deliver consistently for our clients, which is directly related to the quality of the people we hire.\n\nThe past four years, we’ve been in the top 10 “Best Companies to Work For” ….. why? Our team members are highly self-motivated, comfortable conceiving strategies on the fly, and enjoy working both individually and as part of a team. Our environment is very high-energy and demanding, and individuals with remarkable enthusiasm and a can-do attitude are joining our team. We have lots of fun, focus on our employees and our clients, and work to bring our best to every opportunity. | 3.8 | Logic20/20\n3.8 | San Jose, CA | Seattle, WA | 201 - 500 | 2005 | Company - Private | Consulting | Business Services | $25 to $50 million (USD) | -1 | 0 | 0 | 86 | 142 | 114.0 | Logic20/20 | CA | 16 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | data scientist | na | M |
| 8 | 8 | Research Scientist | $38K-$84K (Glassdoor est.) | SUMMARY\n\nThe Research Scientist I will be tasked with oversight of research in the Division of Cancer Biology Research at the Rochester General Hospital Research Institute.\n\nA strong background in Molecular Biology or Cancer Biology Research is preferred. Mouse models will be used in the research.\n\nSTATUS: Full Time\n\nLOCATION: RGH Research Institute\n\nDEPARTMENT: Cancer Biology\n\nSCHEDULE: Monday-Friday; Days\n\nATTRIBUTES\nMD or PhD who is not self supporting of their own salary nor has their own research program\nFunctions with minimal direction from Research Scientist II, Senior Research Scientist or Laboratory Director.\nStrong analytical, computer, leadership and problem-solving skills\nRESPONSIBILITIES\nConducts research projects including complex experiments, some in parallel, utilizing current concepts and recognized standard techniques, developing new protocols as necessary\nDemonstrates a high level of initiative in performing experiments, analyzing data and drawing conclusions regarding progress and results of work.\nMaintains a familiarity with current and emerging technologies through reading and understanding scientific and technical literature resulting in a broadening understanding of disciplines outside area of training and enabling the use of new and improved procedures in the laboratory.\nDuties are performed with an understanding of drug discovery in area of specialization.\nEDUCATION PhD; MD Rochester Regional Health is an Equal Opportunity / Affirmative Action Employer. Minority/Female/Disability/Veteran | 3.3 | Rochester Regional Health\n3.3 | Rochester, NY | Rochester, NY | 10000+ | 2014 | Hospital | Health Care Services & Hospitals | Health Care | $500 million to $1 billion (USD) | -1 | 0 | 0 | 38 | 84 | 61.0 | Rochester Regional Health | NY | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | other scientist | na | P |
| 9 | 9 | Data Scientist | $120K-$160K (Glassdoor est.) | isn’t your usual company. Our work is powered by the premise that every person at is unique, possessing a distinct set of skills, personality, and passions. We embrace our collective talents to tackle technical challenges, refine our successfully disruptive business ideas, and co-create one of the most human and inspiring work cultures out there. We are a team of collaborators, valuing and rewarding shared success over individual heroics.\nAs a member of our Data Science team, you will use your quantitative expertise to identify new areas of research and optimization, and then see those ideas through to production. Data Science is a fundamental contributor to Intent’s success - your work will have a direct and tangible impact on the business.\nThere are no typical projects, but a workflow might involve performing research and analysis against petabytes of historical data using our collection of large-scale analytics tools like Spark, Snowflake, and RedShift, building prototypes using mostly Scala or another functional language, pairing with engineers on the Modeling and Prediction team to harden and deploy the functionality, and running live tests to monitor the results.\nAll of these steps take place in an environment of respect and collaboration, and the Data Science team is empowered to own its agile processes. Every member of the team is expected to be both a student and teacher, and we believe that the most effective Data Science team is one that is collectively learning and growing. Experience in coaching and mentoring colleagues at all levels is strongly desired. As part of the Data Science team, you’d help build out a real-time predictive analytics platform that makes decisions for some of the largest sites on the web.\nAbout You:\nSignificant industry experience in several of the following areas: personalized experiences, big data analytics, implementing machine learning & statistical methods, designing and running A/B tests, product design and life cycle, writing production code, designing online auctions.\nExperience in user experience customization a plus\nExperience coaching and mentoring team members\nExperience writing production software in languages like Scala, Clojure, Java, Python, or C++ in an agile, collaborative environment\nExperience with handling large amounts of data (TB+) in a production setting\nExperience with Spark is a significant plus\nExperience in ad-tech a plus\nAbout Us:\nis the data science company for the world’s leading online commerce and travel brands. Our Predictive Intelligence Platform uses patented technology to predict user behavior in real-time and identify the future value of every user. Over 450 innovative brands from more than 40 countries trust Intent’s real-time predictions to deliver personalized user experiences that maximize utility and ROI.\nOur team is over 100 people and our offices span globally. We’re headquartered in NYC with locations in London, Kuala Lumpur, and Sao Paulo.\nEvery day, we’re inspired by two pursuits. First, we’re building novel products that are upending e-commerce. Second, we’re building the company we’ve always wanted to work for — one that’s open, human and collaborative, where very smart people come together to share ideas and get things done. We’re included on Built in NYC's Best Places to Work list and have been on Crain’s 100 Best Places to Work in NYC list for seven years running.\nLove Your Job!\nOur employees enjoy coming to work, and we let them know they're valued.\nOur vibrant team accomplishes a lot every day, but we insist upon work/life balance so things never become stale. We don’t take ourselves too seriously, but we take our work very seriously.\nWe believe that in order for our employees to perform their best, they need access to strategic decisions, and so our flat structure and open communication invite innovation from all levels — ideas flow freely.\nWe offer competitive compensation, stock options, and great perks & benefits, including:\nUnlimited vacation\nA generous parental leave policy\nA beautiful, dog-friendly office in SoHo with drinks and snacks\nAn open environment with lots of natural light and roof deck access\nAnnual $2,000 learning budget and Citi Bike membership\nAccess to Fond, our employee perks program featuring deals and discounts on hundreds of products and services\nAccess to Sherpaa, a telehealth service with 24/7\nIn-office yoga classes\nCompany-wide social events, and more!\nSo what are you waiting for? Apply with your resume in just a few clicks!\nAbout Us\nOur Products\nOur Dogs\nTwitter\nInstagram | 4.6 | <intent>\n4.6 | New York, NY | New York, NY | 51 - 200 | 2009 | Company - Private | Internet | Information Technology | $100 to $500 million (USD) | Clicktripz, SmarterTravel | 0 | 0 | 120 | 160 | 140.0 | <intent> | NY | 12 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | data scientist | na | na |
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| 732 | 945 | Machine Learning Engineer (NLP) | $80K-$142K (Glassdoor est.) | CK-12’s mission is to provide free access to open-source content and technology tools that empower students as well as teachers to enhance and experiment with different learning styles, resources, levels of competence, and circumstances.\n\nTo achieve this noble and ambitious vision, we at CK-12 are challenging the traditional model of education to transform it dramatically. Technology has opened up lots of opportunities to revolutionize education for the benefit of students, teachers, and parents.\n\nWe have chosen to be non-profit so that we can effectively realize our mission and so that we can do the right thing! It also provides us the ability to experiment with big and bold ideas. CK-12 is backed by Vinod Khosla, a renowned technology venture capitalist.\n\nAt CK-12, you’ll experience the benefits of working in a dynamic, entrepreneurial, innovative and non-bureaucratic environment where you will get a lot of cool things done than you ever imagined! We are a small group of passionate folks who are determined to disrupt the current form of education. We came together from companies such as Apple, eBay, Amazon, McGraw-Hill, and startups.\n\nTechnology is key to scale education and we deeply believe in it. Come develop great solutions on our cloud-based (AWS) AI-first platform delivering rich and interactive content.\n\nDoes our mission, people and technologies excite you? If the answer is YES! and you are a great technologist who will challenge status-quo (no order takers please!) by innovating, please come join us! Together, we will change the world!\n\nCORE RESPONSIBILITIES\nAnalyze textual content and apply NLP to build\nQuestion and Answering System\nNatural Language Generation\nContent Summarization\nLearning Chatbot\nML Assisted Grading System\nExtract and analyze large volumes of data deeply to understand and deduce a wide range of information about CK-12 students, teachers based on their usage history\nApply Machine learning algorithms to\nDiscover patterns in usage\nPredict users behavior\nIdentify student knowledge gaps and misconceptions\nExtract knowledge from CK-12 content using deep learning\nEnvision, experiment, build (or discard), and deliver ML products that can disrupt the Edtech space\nHave fun while driving innovation through ML by challenging the status quo in education and learning and providing creative ML-based solutions\nREQUIREMENTS\nBachelor’s or higher degree in a quantitative discipline (Computer Science or equivalent) or equivalent work experience\nHands-on developer with 3+ years of experience and excellent programming skills (Python is a strong plus)\n3+ years of experience in NLP\nExperience with recent developments in Deep Learning-based NLP\nExperience with a combination of the following:\nQuestion Answering\nKnowledge Graph\nDialog Systems/Conversational systems\nMachine Translation\nNatural Language Generation\nText Summarization\nExperience in building scalable production services\nSkills: Python, TensorFlow, PyTorch, MXNet\nCapacity to handle multiple tasks and prioritize effectively\nAble to translate high-level directions and open-ended questions into practical projects and lead/drive their completion with minimal supervision\nEnvision what ML can do for education\nHOW TO APPLY\n\nSubmit your resume to ml@ck12.org with “Machine Learning Engineer (NLP)” in the subject line.\nIt is a full-time position at our office in Palo Alto, CA (no telecommuting)\nThe applicant must be authorized to work in the US for any employer | 4.1 | CK-12 Foundation\n4.1 | Palo Alto, CA | Palo Alto, CA | 1 - 50 | 2007 | Company - Private | K-12 Education | Education | Unknown / Non-Applicable | -1 | 0 | 0 | 80 | 142 | 111.0 | CK-12 Foundation | CA | 14 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | machine learning engineer | na | na |
| 733 | 946 | Senior Data Analyst | $99K-$178K (Glassdoor est.) | Senior Data Analyst\n\nAbout us\n\n\nLife360 brings families closer with smart tools designed to protect and connect the people who matter most.\n\nKnown for first-to-market solutions for modern family challenges, Life360 recently reached #1 in Apple's US App Store's list of free social networking apps. Nearly 1 in 10 US families with kids use Life360 an average of 12 times a day, and global membership is growing exponentially, with over 25 million monthly active users in over 140 countries making Life360 the largest mobile service for families in the world.\n\nThis reach gives us the opportunity to do unprecedented good for families through our valued core offerings: advanced location sharing, private messaging, driver monitoring, help alerts, 24/7 roadside assistance, and Crash Detection with emergency response. On average we respond to 1,000 roadside assists and dispatch 200+ ambulances each month to those in need.\n\nOffering both free and paid memberships. In addition, the company has raised over $200 million in equity financing, and recently completed an IPO on the ASX exchange giving our employees the liquidity of a public company with the upside of a private growth stage business.\n\nLife360's rapidly growing team of 150+ employees is headquartered in San Francisco, with offices in San Diego, and Las Vegas.\n\nAbout the Job\n\n\nData plays a crucial role in Life360's growth by driving smarter decisions, improving operations, and creating higher value user experiences. As an analytics team, we partner with a wide variety of cross-functional partners to apply data insights against strategic initiatives. "Know Our Users" is a Life360 core value and we're looking for analytics professionals who are passionate about leveraging user data to create value for Life360 families.\n\nYou'll be working in a dynamic growth environment, leading efforts to better understand the business, the product, and the customer. Life360 has one of the most interesting datasets in the world: location, driving, product usage, and purchasing behavioral data - all centered around who matters most, the family. If you have a passion for making an impact and working on products that help millions of families around the world, then this is the right place for you.\n\nResponsibilities\n\n\nAnalytics team members work closely with specific strategic teams but also have opportunities to work on company-wide initiatives. This person is expected to focus on that particular area but also generalize their skills towards other parts of the business with a variety of projects.\n\nIn this role, we are looking for someone to partner with the Revenue team in developing actionable insights from both product and financial perspectives. Common projects range from financial disclosure reports that tell Life360's growth story to conducting deep-dive analyses into identifying opportunities for subscription growth. Ultimately, you will be tasked with finding data insights that deliver business value.\n\nThese are some typical responsibilities:\nLeverage data to understand the Life360 family and their product usage, developing insights that apply to product, marketing, and business strategy.\nPartner with executives, product managers, engineers, marketers, designers to translate data insights into smarter decisions and applications.\nEstablish and manage KPIs that measure the health of the business, product performance, and customer experience quality.\nBuild dashboards and reporting processes to monitor business and product trends.\nDevelop frameworks, tools, and best practices to apply data insights towards business questions.\nConduct analyses and build models that identify opportunities and drive growth.\nDesign and analyze experiments, communicate results, and drive decisions.\nPotential projects may include forecasting business performance, developing family driving profiles, and predicting customer lifetime value.\nQualifications\n\n\nWe are looking for candidates with a diverse background that will compliment the skills and backgrounds of the current team. If you don't fit all the criteria below please apply anyway as this list is more of a preference rather than a rule. Our priority is for a well rounded team that delivers results.\nWe are looking for candidates who have had previous experience on analytics teams and are willing to help coach and mentor colleagues on best data practices. 5+ years is preferred.\nDegree in a quantitative field like statistics, economics, applied math, operations research, or engineering, finance, business intelligence. Advanced degrees are preferred.\nSQL expertise - able to write structured and efficient queries on large datasets.\nExperience in scripting languages, like analysis and visualization libraries in Python or R.\nStrong verbal/written communication skills and the ability to collaborate with cross-functional partners to build the business.\nProficiency in building data visualizations and interactive dashboards with tools like Tableau.\nExperience designing and evaluating experiments to draw inferential recommendations.\nCuriosity to learn about new topics and uncover hidden insights.\nPerks\nFridays are Work From Home days at Life360\nCompetitive pay and benefits\nFree snacks, drinks (three ways to brew your favorite cup of coffee), and food in the office\nCatered lunches throughout the week\nHealth, dental and vision insurance plans\n401k plan\n$200/month Quality of Life perk\nA great office with plenty of light in the heart of the SOMA district in beautiful San Francisco\nWhatever makes you stronger makes us stronger. We buy you the things you need to improve yourself and get your job done.\nThis position is located in San Francisco, CA. It is not a remote role. | 3.9 | Life360\n3.9 | San Francisco, CA | San Francisco, CA | 51 - 200 | 2008 | Company - Public | Computer Hardware & Software | Information Technology | Unknown / Non-Applicable | -1 | 0 | 0 | 99 | 178 | 138.5 | Life360 | CA | 13 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | analyst | sr | na |
| 734 | 947 | Data Science Project Manager | $37K-$100K (Glassdoor est.) | At MassMutual, we are passionate about helping millions of people find financial freedom and this passion has driven our approach to developing meaningful experiences for our customers. The Data Science team, part of the Enterprise Technology and Experience organization, is comprised of highly skilled and collaborative problem solvers who are motivated to create innovative solutions that exceed the changing needs of our customers and move MassMutual and the industry forward.\n\nTo continue our cutting-edge work, we are hiring a Data Science Project Manager to join our team.\n\nWhat great looks like for this role\n\nA seasoned Project Manager will have the opportunity to apply advanced project and program management knowledge, skills, tools and techniques to project deliverables, processes, communications and presentations in order to meet or exceed stakeholder needs and expectations. The Project Manager will have the ability to think strategically to understand, apply, promote and contribute to MassMutual's delivery methodologies, standards and tools. This individual will work with a team that embraces diversity in all of its forms, respects others and looks to have fun.\n\nObjectives of this role\nTo scale our data science impact.\nTo impact complex business goals through the delivery of quality work timely.\nTo ensure documentation is in place and process is followed meeting standards\nDaily and Monthly Responsibilities - What You Will Do:\nLead broad scope projects that have medium to long-term focus\nEngage with all levels across the enterprise\nServe as a conduit of knowledge between functional and technical teams\nCommunicate regularly with individuals both within and outside of our team, managing relationships and expectations\nNavigate ambiguity to deliver results\nDevelop plans for continuous service to support implementation of products\nAct as a champion for data science capabilities by communicating their benefits and how they can be implemented\nProvide consultation, business analysis, project management, and leadership on multiple projects of varying duration, size, and complexity\nMotivate teams to work together, communicate, and deliver\nElicit, translate and simplify requirements\nDocument and organize acceptance criteria for user stories\nManage budget, timeline, and scope throughout the course of all assigned projects\nLead project teams during all phases of the development life cycle including requirements gathering and analysis, design, build, pilot, implementation and continuous service\nFacilitate client and project team interactions including: scrums, sprint planning, sprint retrospectives, sprint reviews, incident management and release management\nWork with product managers to define improvements to business processes, assist decision-makers in gathering information to make decisions, and help quality assurance test solutions\nWork with technical leads, product managers to plan, develop technical scopes of work and manage the execution of projects/product changes in response to requirements from our stakeholders\nBe self-supportive in collaborating with peers to effectively deliver a robust solution for the business\nDrive process within a matrix management setting\nWhat You Will Not Do:\nDesign strategic roadmaps\nLarge amounts of computer programming\nManipulation of large data sets\nSit in solitude at your desk\nBasic Qualifications\nBachelors Degree preferably in Business/Finance or an analytical field such as Economics, Mathematics, Engineering, Computer Science\n4+ years managing and driving the execution of complex projects\nExperience in/working in partnership with a technical role, such as an engineer, developer, data scientist, etc. a plus\nProficient with project management tools and techniques, such as JIRA, Confluence, Scrum and Kanban\nExcellent interpersonal communication, conflict management, coordination, and planning skills with cross-functional teams\nSkilled in applying judgment to balance process compliance with achievement of business objectives\nProject leadership experience focused on engaging others in the delivery and execution of technical solutions and service deliverables\nAbility to assess a project's scope and the team's ability to execute\nOutcome oriented with the ability to drill down from the big picture to process details\nAbility to communicate objectives, plans, status and results clearly\nStrong leadership skills and influencer\nAbility to collaborate across diverse teams and organizations\nStrong organizational skills and detail oriented\nAuthorized to work in the United States without requiring visa sponsorship now or in the future\nPreferred Qualifications\nMasters Degree, preferably in Business/Finance or an analytical field such as Economics, Mathematics, Engineering, Computer Science\nAgile certification or experience\nSolid grasp of software technologies and stacks.\nFormer technical experience is preferred, such as working with data science teams or experience developing and/or deploying predictive models | 3.6 | MassMutual\n3.6 | Boston, MA | Springfield, MA | 5001 - 10000 | 1851 | Company - Private | Insurance Carriers | Insurance | $10+ billion (USD) | -1 | 0 | 0 | 37 | 100 | 68.5 | MassMutual | MA | 170 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | data scientist | na | M |
| 735 | 948 | Data Engineer | $62K-$113K (Glassdoor est.) | Do you find data architecture exciting? Does building a new data pipeline or optimizing a data warehouse make you happy? Can you migrate a data store to the cloud, run a few NLP algorithms to clean things up, and build a set of processes to keep the data current? Are you comfortable with Terabyte-scale data, optimizing cloud stores, building workflow management systems, AWS, and Python scripting? Can you work closely with business stakeholders to understand their needs and sate those through data solutions? If so, we want you!\n\nFivestars is seeking a Senior Data Engineer. Reporting to the Director of Analytics and Data Science, you will work with the Product, Marketing, and Engineering teams at Fivestars to build and maintain world-class data infrastructure.\n\nAt Fivestars, our mission is to help businesses and communities thrive by turning every transaction into a relationship. Over 50 million people use Fivestars to get rewarded at more than 14,000 local businesses with one rewards program. Local businesses use Fivestars to bring more customers into their stores with an all-in-one marketing and payments program. Fivestars drives over $3 billion in local commerce across its network per year.\n\nFivestars was launched out of Y-Combinator in 2011 (most recently on Y-Combinator's Top 75 Companies List for 2019) and has raised over $105 million from notable investors including Lightspeed, DCM, HarbourVest, Menlo Ventures, Y-Combinator, and others. Together, let's love local!\n\nResponsibilities\nBuild and maintain data infrastructure (Redshift/Presto/Kinesis/Glue/EC2/S3/etc.)\nCreate data pipelines to/from external partners using Python and other tools\nUse NLP to clean and consolidate data\nEstablish and use workflow-management tools to orchestrate solutions\nMonitor and improve pipeline and data-warehouse performance\nSkills\nSQL – write sophisticated and optimized queries against large databases\nPython – create efficient and scalable pipelines and solutions\nBusiness Acumen – understand the questions we are trying to answer through data\nProblem Solving – apply structured methods to analyze problems and develop solutions\nCommunication – explain technical concepts clearly and concisely\nRelationships – influence adoption of infrastructure through partnership\nQualifications/Experience\nUndergraduate degree in a highly technical field (e.g. Computer Science, Electrical Engineering, etc.) from a top-tier university\nGraduate degree (MS, PhD, etc.) in a similar field will be highly valued but is not required\n1+ years of experience in a data-engineering function using cloud-based infrastructure\nAbility to solve technical problems and create efficient, robust, and scalable solutions\nDemonstrated intellectual curiosity\nPerks\nPre-IPO stock options\nExcellent medical, dental, and vision coverage\nGreat downtown-SF office location\n4 weeks PTO + 11 paid-holidays per year\nThree in-office lunches per week and a fully-stocked kitchen with fruit, (healthy) snacks, coffee, and drinks\nTeam happy hours and company-sponsored events\nWellness Benefit - $500 per year to spend on eligible physical or mental well being\nFSA; short-/long-term disability coverage; life Insurance; 401K; EAP; and commuter benefits\nFivestars provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability or genetics. In addition to federal law requirements, Fivestars complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. | 3.9 | Fivestars\n3.9 | San Francisco, CA | San Francisco, CA | 201 - 500 | 2011 | Company - Private | Internet | Information Technology | $100 to $500 million (USD) | Belly, SpotOn | 0 | 0 | 62 | 113 | 87.5 | Fivestars | CA | 10 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | data engineer | na | P |
| 736 | 949 | Principal, Data Science - Advanced Analytics | $86K-$137K (Glassdoor est.) | IQVIA is the leading human data science company focused on helping healthcare clients find unparalleled insights and better solutions for patients. Formed through the merger of IMS Health and Quintiles, IQVIA offers a broad range of solutions that harness the power of healthcare data, domain expertise, transformative technology, and advanced analytics to drive healthcare forward.\n\nJob Description\n\nThe IQVIA Advanced Analytics team is one of the leading healthcare analytical teams in the world. Joining the AA team provides the opportunity to work with extremely complex data and methodologies in a fast-paced, ever-changing environment. We seek highly motivated people who truly want to make a difference in the life sciences industry. At IQVIA, we look for the very best people, and then give them meaningful work to do. we dont simply think about careers, we think about contributions.\n\nAdvanced Analytics - with departments in Philadelphia, Frankfurt, Paris, and Warsaw as well as a network of over 150 team members worldwide - is the global competence center for data science at IQVIA. Complex advanced analysis at the highest level are conceptualized and implemented to support international customers in the pharmaceutical industry - often within multinational projects. As a member of our team you can expect exciting international projects with interesting development perspectives.\n\nThe position will use large data sets to find opportunities for product and process optimization and models to test the effectiveness of different courses of action. Our data scientists have strong experience using a variety of data mining/data analysis methods, building and implementing models, using/creating algorithms and simulations. For this position, we are seeking several years of direct experience with developing algorithms and models to solve prediction problems. Awareness of various techniques available to use in predictive analytics. Using their proven ability to drive business results with their data-based insights, they will comfortably interact and work with a wide range of stakeholders and functional teams. They have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.\n\nWhat were looking for:\nQuantitative background with advanced degrees (Master, PhD preferred) in Statistics, computer science, engineering, informatics, data science, or related field.\nIn-depth understanding of machine learning algorithms and statistical models\nAbility to manage, lead and communicate\nExperience in pharmaceutical or hospital/healthcare industry\nWhat youll be doing:\nBuild machine learning/statistical models and pipelines for solving predictive analytic tasks with electronic healthcare claims and medical records\nApply machine learning, data mining technologies in developing innovative solutions in pharmaceutical industry.\nParticipate at client meetings for complex proposals to present IQVIA advanced analytic methodologies to clients and to bring credibility for IQVIA team\nEnsure data quality throughout all stages of acquisition and processing, including such areas as data collection, normalization, transformation, embedding, visualization, etc.\nPresent study findings to clients and translate analytic outputs to business impact and recommend actions to clients to improve their business performance\nEnsure data quality throughout all stages of acquisition and processing, including such areas as data collection, normalization, transformation, embedding, visualization, etc.\nWork with IQVIA technology team to support machine-learning algorithms in big data platform to solve a variety of business problems.\nIQVIA is an EEO Employer - Minorities/Females/Protected Veterans/Disabled\n\nWe know that meaningful results require not only the right approach but also the right people. Regardless of your role, we invite you to reimagine healthcare with us. You will have the opportunity to play an important part in helping our clients drive healthcare forward and ultimately improve human health outcomes.\n\nWhatever your career goals, we are here to ensure you get there!\n\nWe invite you to join IQVIA.\n\nJoin Us\n\nMaking a positive impact on human health takes insight, curiosity, and intellectual courage. It takes brave minds, pushing the boundaries to transform healthcare. Regardless of your role, you will have the opportunity to play an important part in helping our clients drive healthcare forward and ultimately improve outcomes for patients.\n\nForge a career with greater purpose, make an impact, and never stop learning.\n\nIQVIA is an EEO Employer - Minorities/Females/Protected Veterans/Disabled\n\nIQVIA, Inc. provides reasonable accommodations for applicants with disabilities. Applicants who require reasonable accommodation to submit an application for employment or otherwise participate in the application process should contact IQVIAs Talent Acquisition team at workday_recruiting@iqvia.com to arrange for such an accommodation. | 3.6 | IQVIA\n3.6 | Plymouth Meeting, PA | Durham, NC | 10000+ | 2017 | Company - Public | Biotech & Pharmaceuticals | Biotech & Pharmaceuticals | $2 to $5 billion (USD) | PPD, INC Research, PRA Health Sciences | 0 | 0 | 86 | 137 | 111.5 | IQVIA | PA | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | data scientist | sr | M |
| 737 | 950 | Sr Scientist, Immuno-Oncology - Oncology | $58K-$111K (Glassdoor est.) | Site Name: USA - Massachusetts - Cambridge\nPosted Date: Mar 24 2020\n\nAre you energized by a challenging role in immuno-oncology, where scientific demand is driving team growth? If so, this Senior Scientist would be a great opportunity to consider.\n\nThe Immune Biology Group within GSKs Immuno-Oncology & Combinations Research Unit (IOC RU) is seeking a Sr. Scientist with experience in immuno-oncology or immunology to join our team.\n\nIn this role, you will be responsible for conducting research designed to identify and validate immune-based therapies for cancer.\n\nThis Sr. Scientist role will provide you the opportunity to lead key activities to progress your career. Responsibilities include:\nDeliver critical path biology results to support GSKs pipeline of cancer immunotherapies from early discovery to first-time-in-human commitment.\nEstablish and expand internal wet lab capabilities at a growing GSK site.\nActively participate in building and maintaining drug discovery relationships with both internal stakeholders and external partners.\nWork within a dynamic and collaborative environment to deliver high-quality scientific data packages to meet experimental and organizational goals.\nWhy you?\nBasic Qualifications:\n\n\nWe are looking for professionals with these required skills to achieve our goals:\nBachelors or Masters degree in immunology, immuno-oncology or related field with 5+/3+ years of experience, respectively.\nStrong scientific background in immunology or immuno-oncology research, with a focus on bioassay development to functionally characterize biologics and/or small molecules.\nResearch expertise in the field of adaptive immunity with a focus on T cell biology with demonstrated ability to independently establish robust in vitro and ex vivo functional assay protocols to investigate mechanisms of action for multiple drug candidates and their combinations.\nExpertise in high-dimensional flow cytometry to phenotypically characterize immune cells from human and murine tissue samples, including both surface and intracellular staining.\nDemonstrated hands-on ability to independently design, conduct, and analyze pharmacology studies.\nStrong communication skills and ability to conduct research in a cross-functional team environment.\nAbility to interpret data clearly and concisely both verbally and in documents and present results in an organized manner.\nAbility to prioritize, manage time efficiently, and implement creative solutions to meet program needs.\nCommitment to continual improvement by reading and applying the latest scientific literature, methodologies and technology where appropriate.\nA high level of integrity and desire to develop transformational medicines that bring benefit to patients\nPreferred Qualifications:\n\n\nIf you have the following characteristics, it would be a plus:\n2+ years pharmaceutical or biotechnology industry research experience working in matrixed drug discovery project teams.\nResearch expertise with functional characterization of myeloid cells\nExperience liaising with Laboratory Operations personnel.\nWhy GSK?\n\nOur values and expectations are at the heart of everything we do and form an important part of our culture. These include Patient focus, Transparency, Respect, Integrity along with Courage, Accountability, Development, and Teamwork. As GSK focuses on our values and expectations and a culture of innovation, performance, and trust, the successful candidate will demonstrate the following capabilities:\nOperating at pace and agile decision-making using evidence and applying judgement to balance pace, rigour and risk.\nCommitted to delivering high quality results, overcoming challenges, focusing on what matters, execution.\nContinuously looking for opportunities to learn, build skills and share learning.\nSustaining energy and well-being\nBuilding strong relationships and collaboration, honest and open conversations.\nBudgeting and cost-consciousness\n*LI-GSK\n\n*This is a job description to aide in the job posting, but does not include all job evaluation\n\nIf you require an accommodation or other assistance to apply for a job at GSK, please contact the GSK Service Centre at 1-877-694-7547 (US Toll Free) or +1 801 567 5155 (outside US).\n\nGSK is an Equal Opportunity Employer and, in the US, we adhere to Affirmative Action principles. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, color, national origin, religion, sex, pregnancy, marital status, sexual orientation, gender identity/expression, age, disability, genetic information, military service, covered/protected veteran status or any other federal, state or local protected class.\n\nImportant notice to Employment businesses/ Agencies\n\nGSK does not accept referrals from employment businesses and/or employment agencies in respect of the vacancies posted on this site. All employment businesses/agencies are required to contact GSK's commercial and general procurement/human resources department to obtain prior written authorization before referring any candidates to GSK. The obtaining of prior written authorization is a condition precedent to any agreement (verbal or written) between the employment business/ agency and GSK. In the absence of such written authorization being obtained any actions undertaken by the employment business/agency shall be deemed to have been performed without the consent or contractual agreement of GSK. GSK shall therefore not be liable for any fees arising from such actions or any fees arising from any referrals by employment businesses/agencies in respect of the vacancies posted on this site.\n\nPlease note that if you are a US Licensed Healthcare Professional or Healthcare Professional as defined by the laws of the state issuing your license, GSK may be required to capture and report expenses GSK incurs, on your behalf, in the event you are afforded an interview for employment. This capture of applicable transfers of value is necessary to ensure GSKs compliance to all federal and state US Transparency requirements. For more information, please visit GSKs Transparency Reporting For the Record site. | 3.9 | GSK\n3.9 | Cambridge, MA | Brentford, United Kingdom | 10000+ | 1830 | Company - Public | Biotech & Pharmaceuticals | Biotech & Pharmaceuticals | $10+ billion (USD) | Pfizer, AstraZeneca, Merck | 0 | 0 | 58 | 111 | 84.5 | GSK | MA | 191 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | other scientist | sr | M |
| 738 | 951 | Senior Data Engineer | $72K-$133K (Glassdoor est.) | THE CHALLENGE\nEventbrite has a world-class data repository of live events, powering millions of events and hundreds of millions of ticket transactions each year in 170+ countries. Our platform allows event creators and event goers to have the most meaningful live experiences. As a Senior Data Engineer, you will be part of a team that is building our next-gen big data infrastructure to support both internal and customer-facing applications.\nTHE TEAM\nWe're a people-focused Engineering organization: our people value working together in small teams to solve significant problems, supporting an active culture of mentorship and inclusion, and pushing themselves to learn new things daily. Pair programming, weekly demos, tech talks, and quarterly hackathons are at the core of how we’ve built our team and product. We believe in engaging with the community, regularly hosting free events with some of the top technical speakers, and actively contributing to open source software (check out Britecharts as an example!). Our technology spans the web, mobile, API, Big Data, machine learning, search, physical point of sale, scanning systems, and the data infrastructure required to support those systems.\nTHE ROLE\nWe are hiring a Senior Data Engineer to help us build a scalable, reliable, secure, and highly performant data platform. You'll help reinforce and extend the infrastructure that powers the use of data at Eventbrite. From infrastructure development to data analysis to ETL jobs, you will need a broad range of big data engineering skills. The team has strong and versatile engineers. You will grow. We hope to grow with you.\nTHE SKILL SET\n8-10 years of experience building high quality software in Python, Java, or Scala\n5+ years of experience designing batch, streaming, and event-driven Data Warehouse and ETL architectures with Hadoop ecosystem, such as Spark, Hive, Storm, Presto, Kafka, Hbase, MySQL databases, and HDFS\nUnderstanding of Data Engineering, Data Science, Machine Learning, Data Analytics, and the relevant technologies that support them\nDeep expertise in cloud computing, preferably AWS, security, cluster sizing, and performance tuning. Ability to setup process and systems to monitor and reduce cloud computing costs for a large organization\nExperience building systems to instrument, collect and process billions of events, such as clickstream data. Deep understanding of measuring and ensuring data quality at scale\nOutstanding verbal, written, presentation, and facilitation skills. In particular, a demonstrated ability to effectively communicate technical and business issues and solutions to multiple organizational levels\nAbility to teach and mentor engineers with a variety of skill levels and backgrounds\nVision to define the future of how Big Data and Analytics intersect at Eventbrite. The Analytics community at Eventbrite will rely on you to build and maintain a data environment built for speed, accuracy, consistency and uptime\nSkills to support analytics by building a world-class data warehousing environment that empowers analysts to deliver insights to their stakeholders. Evaluate competing data technologies and toolsets from various vendors and open-source products; drive platform selection; lead technical architecture, application design and implementation\nSkills to support analytics by building a world class data warehousing environment that empowers analysts to deliver insights to their stakeholders\nEvaluate competing data technologies and toolsets from various vendors and open-source products; drive platform selection; lead technical architecture, application design and implementation\nCombine strong analytical skills with the ability to collect, organize and analyze large amounts of information with attention to detail and accuracy\nPassionate about live entertainment, and eager to help build Eventbrite into the world's leading event technology platform\nStrong analytical and problem-solving skills and attention to detail\n\nBONUS POINTS\nFamiliarity with a server-side frameworks, such as Django, Express, Rails, or .Net\nSkilled in various forms of data modeling including ER, XML Schemas, SQL, logical and physical database design, dimensional modeling, and/or OLAP cubes\nKnowledge of database schemas and models, including 3NF, star schemas, cubes, etc. and in developing physical database schemas from logical models\nStrong knowledge of database optimization and scaling approaches including indexing, partitioning, sharding, clustering, in memory tables, horizontal and vertical scaling\nFamiliarity with managing large datasets and understanding the complexities of merging large databases, meeting security audit requirements, and implementing a data retention policies\n\nABOUT EVENTBRITE\nEventbrite is a global ticketing and event technology platform, powering millions of live experiences each year. We empower creators of events of all shapes and sizes – from music festivals, experiential yoga, political rallies to gaming competitions –– by providing them the tools and resources they need to seamlessly plan, promote, and produce live experiences around the world. Last year, the team served 795,000 creators hosting nearly 4 million experiences across 170 countries. Meet some of the Britelings that make it happen.\n\nIS THIS ROLE NOT AN EXACT FIT?\nSign up to keep in touch and we’ll let you know when we have new positions on our team.\n\n\nEventbrite is a proud equal opportunity/affirmative action employer supporting workforce diversity. We do not discriminate based upon race, ethnicity, ancestry, citizenship status, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), marital status, registered domestic partner status, caregiver status, sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, genetic information, military or veteran status, mental or physical disability, political affiliation, status as a victim of domestic violence, assault or stalking, or other applicable legally protected characteristics.\nApplicant Privacy Notice | 4.4 | Eventbrite\n4.4 | Nashville, TN | San Francisco, CA | 1001 - 5000 | 2006 | Company - Public | Internet | Information Technology | $100 to $500 million (USD) | See Tickets, TicketWeb, Vendini | 0 | 0 | 72 | 133 | 102.5 | Eventbrite | TN | 15 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | data engineer | sr | na |
| 739 | 952 | Project Scientist - Auton Lab, Robotics Institute | $56K-$91K (Glassdoor est.) | The Auton Lab at Carnegie Mellon University is a large academic group driven by a desire to make a real-world difference in a broad range of research interests. The areas of our current focus include, but are not limited to, modeling complex temporal and sequential data, structural learning, incorporating diverse feedback, interactive network science and human-machine interaction. We are always interested in finding ways to make Artificial Intelligence more accessible, beneficial and affordable to everyone. The areas of our current application interests include healthcare in clinical, managerial, and new sensing modalities contexts, radiation safety, countering human trafficking, agriculture, predictive maintenance of equipment, multi-modal data analytics, etc.\n\nWe are seeking a Project Scientist to join us in the Auton Lab. In this role, you will act as a team leader for specific areas of research projects in applied data science. Working with principal investigator(s), you will prioritize project goals based on overall organizational goals. You will contribute significantly in the development and documentation of research finding and as a major collaborator of scientific papers. There will be frequent opportunities to present research finding to current or potential sponsors and at major national and international conferences.\n\nCore responsibilities will include:\nPreparing data, developing models, and producing research findings\nContributing to project management and maintenance of customer relationships\nDocumenting research findings, producing reports and synthetic summaries\nContributing to scientific publications\nWorking with principal investigator(s) to formulate research goals and plans\nPreparing and delivering presentation of research findings\nQualifications:\nPhD in machine learning, applied mathematics, statistics, computer science, or other relevant field or equivalent combination of training and experience preferred\n10-15 years of Research Experience required\nProven technical background\nExperience in analyzing of data at scale, proven hands-on model development\nFlexibility, excellence, and passion are vital qualities within Auton Lab. Inclusion, collaboration and cultural sensitivity are valued proficiencies at CMU. Therefore, we are in search of a team member who is able to effectively interact with a varied population of internal and external partners at a high level of integrity. We are especially interested in qualified candidates who can contribute through their work/life experiences to the diversity and excellence of the academic community.\n\nYou should demonstrate:\nExcellent communication skills\nAbility to work optimally in a team\nAre you interested in this opportunity with us? Please apply.\n\nMore Information:\n\nPlease visit “Why Carnegie Mellon” to learn more about becoming part of an institution inspiring innovations that change the world.\n\nA listing of employee benefits is available at: www.cmu.edu/jobs/benefits-at-a-glance/.\n\nCarnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran. | 2.6 | Software Engineering Institute\n2.6 | Pittsburgh, PA | Pittsburgh, PA | 501 - 1000 | 1984 | College / University | Colleges & Universities | Education | Unknown / Non-Applicable | -1 | 0 | 0 | 56 | 91 | 73.5 | Software Engineering Institute | PA | 37 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | other scientist | na | P |
| 740 | 953 | Data Science Manager | $95K-$160K (Glassdoor est.) | Data Science ManagerResponsibilities:\n\nOversee a team of Data Scientists and Data Visualization Analysts who transform enterprise data into value drive insights\n\nDesign and implement processes for complex large-scale datasets for data mining, predictive modeling, and research purposes\n\nServe as an advisor for business stakeholders identifying data needs and explaining the importance and use of data applicable to their usage\n\nOversee development of a style guide detailing best practices standards for data visualization\n\nManage the intake process of analytics projects, measure value, and prioritize projects\n\nAlign the department as a customer-oriented service providing insights and information\n\nCoach and mentor team providing specific, timely and constructive feedback\n\nProvide day-to-day leadership and operational management in area of responsibility\n\nExecute objective, plans, and policies in line with enterprise level strategy\n\nProactively find new opportunities to leverage technology for continuous improvement and greater efficiency\n\nContribute to budget development and assist in preparation of operational plans for department\n\nOversee area of responsibility to adhere to approved budgets\n\nMS degree in a quantitative discipline plus a minimum of 5 years of professional work experience\n\nMinimum of 3 years of management experience\n\nProfessional work experience with R and advanced statistical modeling techniques including machine learning techniques\n\nExcellent oral and written communication skills\n\nExcitement, curiosity and passion for shaping the future through digital technology\n\nUS Citizenship or green card required | 3.2 | Numeric, LLC\n3.2 | Allentown, PA | Chadds Ford, PA | 1 - 50 | -1 | Company - Private | Staffing & Outsourcing | Business Services | $5 to $10 million (USD) | -1 | 0 | 0 | 95 | 160 | 127.5 | Numeric, LLC | PA | -1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | data scientist | na | na |
| 741 | 955 | Research Scientist – Security and Privacy | $61K-$126K (Glassdoor est.) | Returning Candidate? Log back in to the Career Portal and click on 'Job Browsing/History' and find the job you're looking for.\n\n2019-024-OIC: Research Scientist – Security and Privacy\n\nDirectorate Open Innovation Center\nLocation Beavercreek, OH\nIf you want help develop the future technology to ensure security and privacy, Riverside Research’s Trusted and Resilient Systems group is the place for you. We are searching for an individual to join our research group to help shape a more secure future. The team has ongoing research in security of machine learning, cryptography, hardware and hypervisor security solutions, as well as developing cutting edge solutions to the security of open architecture systems. The ideal person for this position is passionate about many diverse areas technology and can leverage their interests to develop and study creative solutions to some of the most difficult challenges. The current team resides in Riverside Research’s Beavercreek, OH, but we are willing to consider candidates that would prefer to work out of one of our Washington DC (Centerville or Crystal City) offices, our New York City office, or our Boston office.\n\nJob Responsibilities:\n•Work with a team of highly skilled researchers to develop interesting and novel solutions to security and privacy problems\n•Publish and present research in conferences and journals\n•Work with the team to identify future areas of research investment and develop research plans\n•Assist with writing technical proposals\n\nQualifications:\n•Ability to obtain and maintain TS/SCI security clearance\n•Bachelor's or Master's degree with significant experience in security privacy research\n•Prior experience developing software\n•Ability to work independently and with a team\n•Superior written and verbal communication skills\nDesired Qualifications:\n\n•Python\n•Web development (we use React)\n•Revision control (we use Git)\n•Machine learning\n•Cryptography\n•Prior experience with government funded research\n\nRiverside Research strives to be one of America’s premier providers of independent, trusted technical and scientific expertise. As we continue to add experienced, technically astute staff, we are looking for highly motivated, talented team members that can help our DoD and Intelligence Community (IC) customers continue delivery of world class programs. As a not-for-profit, technology-oriented Defense Company, we believe service to customers and support of our staff is our mission. Our goal is to serve as a destination company by providing an industry-leading, positive, and rewarding employee experience for all who join us. We aspire to be a valued partner to our customers and to earn their trust through our unwavering commitment to achieve timely, innovative, cost-effective and mission-focused solutions.\n\nAll positions at Riverside Research are subject to background investigations. Employment is contingent upon successful completion of a background investigation including criminal history and identity check.\n\nThis contractor and subcontractor shall abide by the requirements of 41 CFR 60-741.5(a). This regulation prohibits discrimination against qualified individuals on the basis of disability, and requires affirmative action by covered prime contractors and subcontractors to employ and advance in employment qualified individuals with disabilities.\n\nThis contractor and subcontractor shall abide by the requirements of 41 CFR 60-300.5(a). This regulation prohibits discrimination against qualified protected veterans, and requires affirmative action by covered contractors and subcontractors to employ and advance in employment qualified protected veterans.\n\nApply Now | 3.6 | Riverside Research Institute\n3.6 | Beavercreek, OH | Arlington, VA | 501 - 1000 | 1967 | Nonprofit Organization | Federal Agencies | Government | $50 to $100 million (USD) | -1 | 0 | 0 | 61 | 126 | 93.5 | Riverside Research Institute | OH | 54 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | other scientist | na | M |